Merge branch 'dev' into feat/pause-functionality

ref: N25B-350

.env.example

@@ -1,20 +0,0 @@
-# Example .env file. To use, make a copy, call it ".env" (i.e. removing the ".example" suffix), then you edit values.
-
-# The hostname of the Robot Interface. Change if the Control Backend and Robot Interface are running on different computers.
-RI_HOST="localhost"
-
-# URL for the local LLM API. Must be an API that implements the OpenAI Chat Completions API, but most do.
-LLM_SETTINGS__LOCAL_LLM_URL="http://localhost:1234/v1/chat/completions"
-
-# Name of the local LLM model to use.
-LLM_SETTINGS__LOCAL_LLM_MODEL="gpt-oss"
-
-# Number of non-speech chunks to wait before speech ended. A chunk is approximately 31 ms. Increasing this number allows longer pauses in speech, but also increases response time.
-BEHAVIOUR_SETTINGS__VAD_NON_SPEECH_PATIENCE_CHUNKS=15
-
-# Timeout in milliseconds for socket polling. Increase this number if network latency/jitter is high, often the case when using Wi-Fi. Perhaps 500 ms. A symptom of this issue is transcriptions getting cut off.
-BEHAVIOUR_SETTINGS__SOCKET_POLLER_TIMEOUT_MS=100
-
-
-
-# For an exhaustive list of options, see the control_backend.core.config module in the docs.

.gitignore

@@ -222,8 +222,6 @@ __marimo__/
 docs/*
 !docs/conf.py
 
-# Generated files
-agentspeak.asl
 
 
 

README.md

@@ -27,7 +27,6 @@ This + part might differ based on what model you choose.
 copy the model name in the module loaded and replace local_llm_modelL. In settings.
 
 
-
 ## Running
 To run the project (development server), execute the following command (while inside the root repository):
 
@@ -35,14 +34,6 @@ To run the project (development server), execute the following command (while in
 uv run fastapi dev src/control_backend/main.py
 ```
 
-### Environment Variables
-
-You can use environment variables to change settings. Make a copy of the [`.env.example`](.env.example) file, name it `.env` and put it in the root directory. The file itself describes how to do the configuration.
-
-For an exhaustive list of environment options, see the `control_backend.core.config` module in the docs.
-
-
-
 ## Testing
 Testing happens automatically when opening a merge request to any branch. If you want to manually run the test suite, you can do so by running the following for unit tests:
 

src/control_backend/agents/actuation/robot_gesture_agent.py

@@ -33,7 +33,7 @@ class RobotGestureAgent(BaseAgent):
     def __init__(
         self,
         name: str,
-        address: str,
+        address=settings.zmq_settings.ri_command_address,
         bind=False,
         gesture_data=None,
         single_gesture_data=None,
@@ -83,8 +83,6 @@ class RobotGestureAgent(BaseAgent):
             self.subsocket.close()
         if self.pubsocket:
             self.pubsocket.close()
-        if self.repsocket:
-            self.repsocket.close()
         await super().stop()
 
     async def handle_message(self, msg: InternalMessage):

src/control_backend/agents/bdi/agentspeak_generator.py

@@ -1,514 +0,0 @@
-from functools import singledispatchmethod
-
-from slugify import slugify
-
-from control_backend.agents.bdi.agentspeak_ast import (
-    AstAtom,
-    AstBinaryOp,
-    AstExpression,
-    AstLiteral,
-    AstNumber,
-    AstPlan,
-    AstProgram,
-    AstRule,
-    AstStatement,
-    AstString,
-    AstVar,
-    BinaryOperatorType,
-    StatementType,
-    TriggerType,
-)
-from control_backend.schemas.program import (
-    BaseGoal,
-    BasicNorm,
-    ConditionalNorm,
-    GestureAction,
-    Goal,
-    InferredBelief,
-    KeywordBelief,
-    LLMAction,
-    LogicalOperator,
-    Norm,
-    Phase,
-    PlanElement,
-    Program,
-    ProgramElement,
-    SemanticBelief,
-    SpeechAction,
-    Trigger,
-)
-
-
-class AgentSpeakGenerator:
-    _asp: AstProgram
-
-    def generate(self, program: Program) -> str:
-        self._asp = AstProgram()
-
-        if program.phases:
-            self._asp.rules.append(AstRule(self._astify(program.phases[0])))
-        else:
-            self._asp.rules.append(AstRule(AstLiteral("phase", [AstString("end")])))
-
-        self._asp.rules.append(AstRule(AstLiteral("!notify_cycle")))
-
-        self._add_keyword_inference()
-        self._add_default_plans()
-
-        self._process_phases(program.phases)
-
-        self._add_fallbacks()
-
-        return str(self._asp)
-
-    def _add_keyword_inference(self) -> None:
-        keyword = AstVar("Keyword")
-        message = AstVar("Message")
-        position = AstVar("Pos")
-
-        self._asp.rules.append(
-            AstRule(
-                AstLiteral("keyword_said", [keyword]),
-                AstLiteral("user_said", [message])
-                & AstLiteral(".substring", [keyword, message, position])
-                & (position >= 0),
-            )
-        )
-
-    def _add_default_plans(self):
-        self._add_reply_with_goal_plan()
-        self._add_say_plan()
-        self._add_reply_plan()
-        self._add_notify_cycle_plan()
-
-    def _add_reply_with_goal_plan(self):
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                AstLiteral("reply_with_goal", [AstVar("Goal")]),
-                [AstLiteral("user_said", [AstVar("Message")])],
-                [
-                    AstStatement(StatementType.ADD_BELIEF, AstLiteral("responded_this_turn")),
-                    AstStatement(
-                        StatementType.DO_ACTION,
-                        AstLiteral(
-                            "findall",
-                            [AstVar("Norm"), AstLiteral("norm", [AstVar("Norm")]), AstVar("Norms")],
-                        ),
-                    ),
-                    AstStatement(
-                        StatementType.DO_ACTION,
-                        AstLiteral(
-                            "reply_with_goal", [AstVar("Message"), AstVar("Norms"), AstVar("Goal")]
-                        ),
-                    ),
-                ],
-            )
-        )
-
-    def _add_say_plan(self):
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                AstLiteral("say", [AstVar("Text")]),
-                [],
-                [
-                    AstStatement(StatementType.ADD_BELIEF, AstLiteral("responded_this_turn")),
-                    AstStatement(StatementType.DO_ACTION, AstLiteral("say", [AstVar("Text")])),
-                ],
-            )
-        )
-
-    def _add_reply_plan(self):
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                AstLiteral("reply"),
-                [AstLiteral("user_said", [AstVar("Message")])],
-                [
-                    AstStatement(StatementType.ADD_BELIEF, AstLiteral("responded_this_turn")),
-                    AstStatement(
-                        StatementType.DO_ACTION,
-                        AstLiteral(
-                            "findall",
-                            [AstVar("Norm"), AstLiteral("norm", [AstVar("Norm")]), AstVar("Norms")],
-                        ),
-                    ),
-                    AstStatement(
-                        StatementType.DO_ACTION,
-                        AstLiteral("reply", [AstVar("Message"), AstVar("Norms")]),
-                    ),
-                ],
-            )
-        )
-
-    def _add_notify_cycle_plan(self):
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                AstLiteral("notify_cycle"),
-                [],
-                [
-                    AstStatement(
-                        StatementType.DO_ACTION,
-                        AstLiteral(
-                            "findall",
-                            [AstVar("Norm"), AstLiteral("norm", [AstVar("Norm")]), AstVar("Norms")],
-                        ),
-                    ),
-                    AstStatement(
-                        StatementType.DO_ACTION, AstLiteral("notify_norms", [AstVar("Norms")])
-                    ),
-                    AstStatement(StatementType.DO_ACTION, AstLiteral("wait", [AstNumber(100)])),
-                    AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("notify_cycle")),
-                ],
-            )
-        )
-
-    def _process_phases(self, phases: list[Phase]) -> None:
-        for curr_phase, next_phase in zip([None] + phases, phases + [None], strict=True):
-            if curr_phase:
-                self._process_phase(curr_phase)
-            self._add_phase_transition(curr_phase, next_phase)
-
-        # End phase behavior
-        # When deleting this, the entire `reply` plan and action can be deleted
-        self._asp.plans.append(
-            AstPlan(
-                type=TriggerType.ADDED_BELIEF,
-                trigger_literal=AstLiteral("user_said", [AstVar("Message")]),
-                context=[AstLiteral("phase", [AstString("end")])],
-                body=[
-                    AstStatement(
-                        StatementType.DO_ACTION, AstLiteral("notify_user_said", [AstVar("Message")])
-                    ),
-                    AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("reply")),
-                ],
-            )
-        )
-
-    def _process_phase(self, phase: Phase) -> None:
-        for norm in phase.norms:
-            self._process_norm(norm, phase)
-
-        self._add_default_loop(phase)
-
-        previous_goal = None
-        for goal in phase.goals:
-            self._process_goal(goal, phase, previous_goal, main_goal=True)
-            previous_goal = goal
-
-        for trigger in phase.triggers:
-            self._process_trigger(trigger, phase)
-
-    def _add_phase_transition(self, from_phase: Phase | None, to_phase: Phase | None) -> None:
-        if from_phase is None:
-            return
-        from_phase_ast = self._astify(from_phase)
-        to_phase_ast = (
-            self._astify(to_phase) if to_phase else AstLiteral("phase", [AstString("end")])
-        )
-
-        check_context = [from_phase_ast]
-        if from_phase:
-            for goal in from_phase.goals:
-                check_context.append(self._astify(goal, achieved=True))
-
-        force_context = [from_phase_ast]
-
-        body = [
-            AstStatement(
-                StatementType.DO_ACTION,
-                AstLiteral(
-                    "notify_transition_phase",
-                    [
-                        AstString(str(from_phase.id)),
-                        AstString(str(to_phase.id) if to_phase else "end"),
-                    ],
-                ),
-            ),
-            AstStatement(StatementType.REMOVE_BELIEF, from_phase_ast),
-            AstStatement(StatementType.ADD_BELIEF, to_phase_ast),
-        ]
-
-        # if from_phase:
-        #     body.extend(
-        #         [
-        #             AstStatement(
-        #                 StatementType.TEST_GOAL, AstLiteral("user_said", [AstVar("Message")])
-        #             ),
-        #             AstStatement(
-        #                 StatementType.REPLACE_BELIEF, AstLiteral("user_said", [AstVar("Message")])
-        #             ),
-        #         ]
-        #     )
-
-        # Check
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                AstLiteral("transition_phase"),
-                check_context,
-                [
-                    AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("force_transition_phase")),
-                ],
-            )
-        )
-
-        # Force
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL, AstLiteral("force_transition_phase"), force_context, body
-            )
-        )
-
-    def _process_norm(self, norm: Norm, phase: Phase) -> None:
-        rule: AstRule | None = None
-
-        match norm:
-            case ConditionalNorm(condition=cond):
-                rule = AstRule(
-                    self._astify(norm),
-                    self._astify(phase) & self._astify(cond)
-                    | AstAtom(f"force_{self.slugify(norm)}"),
-                )
-            case BasicNorm():
-                rule = AstRule(self._astify(norm), self._astify(phase))
-
-        if not rule:
-            return
-
-        self._asp.rules.append(rule)
-
-    def _add_default_loop(self, phase: Phase) -> None:
-        actions = []
-
-        actions.append(
-            AstStatement(
-                StatementType.DO_ACTION, AstLiteral("notify_user_said", [AstVar("Message")])
-            )
-        )
-        actions.append(AstStatement(StatementType.REMOVE_BELIEF, AstLiteral("responded_this_turn")))
-        actions.append(AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("check_triggers")))
-
-        for goal in phase.goals:
-            actions.append(AstStatement(StatementType.ACHIEVE_GOAL, self._astify(goal)))
-
-        actions.append(AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("transition_phase")))
-
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_BELIEF,
-                AstLiteral("user_said", [AstVar("Message")]),
-                [self._astify(phase)],
-                actions,
-            )
-        )
-
-    def _process_goal(
-        self,
-        goal: Goal,
-        phase: Phase,
-        previous_goal: Goal | None = None,
-        continues_response: bool = False,
-        main_goal: bool = False,
-    ) -> None:
-        context: list[AstExpression] = [self._astify(phase)]
-        context.append(~self._astify(goal, achieved=True))
-        if previous_goal and previous_goal.can_fail:
-            context.append(self._astify(previous_goal, achieved=True))
-        if not continues_response:
-            context.append(~AstLiteral("responded_this_turn"))
-
-        body = []
-        if main_goal:  # UI only needs to know about the main goals
-            body.append(
-                AstStatement(
-                    StatementType.DO_ACTION,
-                    AstLiteral("notify_goal_start", [AstString(self.slugify(goal))]),
-                )
-            )
-
-        subgoals = []
-        for step in goal.plan.steps:
-            body.append(self._step_to_statement(step))
-            if isinstance(step, Goal):
-                subgoals.append(step)
-
-        if not goal.can_fail and not continues_response:
-            body.append(AstStatement(StatementType.ADD_BELIEF, self._astify(goal, achieved=True)))
-
-        self._asp.plans.append(AstPlan(TriggerType.ADDED_GOAL, self._astify(goal), context, body))
-
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                self._astify(goal),
-                context=[],
-                body=[AstStatement(StatementType.EMPTY, AstLiteral("true"))],
-            )
-        )
-
-        prev_goal = None
-        for subgoal in subgoals:
-            self._process_goal(subgoal, phase, prev_goal)
-            prev_goal = subgoal
-
-    def _step_to_statement(self, step: PlanElement) -> AstStatement:
-        match step:
-            case Goal() | SpeechAction() | LLMAction() as a:
-                return AstStatement(StatementType.ACHIEVE_GOAL, self._astify(a))
-            case GestureAction() as a:
-                return AstStatement(StatementType.DO_ACTION, self._astify(a))
-
-    # TODO: separate handling of keyword and others
-    def _process_trigger(self, trigger: Trigger, phase: Phase) -> None:
-        body = []
-        subgoals = []
-
-        body.append(
-            AstStatement(
-                StatementType.DO_ACTION,
-                AstLiteral("notify_trigger_start", [AstString(self.slugify(trigger))]),
-            )
-        )
-        for step in trigger.plan.steps:
-            body.append(self._step_to_statement(step))
-            if isinstance(step, Goal):
-                step.can_fail = False  # triggers are continuous sequence
-                subgoals.append(step)
-
-        # Arbitrary wait for UI to display nicely
-        body.append(AstStatement(StatementType.DO_ACTION, AstLiteral("wait", [AstNumber(2000)])))
-
-        body.append(
-            AstStatement(
-                StatementType.DO_ACTION,
-                AstLiteral("notify_trigger_end", [AstString(self.slugify(trigger))]),
-            )
-        )
-
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                AstLiteral("check_triggers"),
-                [self._astify(phase), self._astify(trigger.condition)],
-                body,
-            )
-        )
-
-        # Force trigger (from UI)
-        self._asp.plans.append(AstPlan(TriggerType.ADDED_GOAL, self._astify(trigger), [], body))
-
-        for subgoal in subgoals:
-            self._process_goal(subgoal, phase, continues_response=True)
-
-    def _add_fallbacks(self):
-        # Trigger fallback
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                AstLiteral("check_triggers"),
-                [],
-                [AstStatement(StatementType.EMPTY, AstLiteral("true"))],
-            )
-        )
-
-        # Phase transition fallback
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                AstLiteral("transition_phase"),
-                [],
-                [AstStatement(StatementType.EMPTY, AstLiteral("true"))],
-            )
-        )
-
-        # Force phase transition fallback
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                AstLiteral("force_transition_phase"),
-                [],
-                [AstStatement(StatementType.EMPTY, AstLiteral("true"))],
-            )
-        )
-
-    @singledispatchmethod
-    def _astify(self, element: ProgramElement) -> AstExpression:
-        raise NotImplementedError(f"Cannot convert element {element} to an AgentSpeak expression.")
-
-    @_astify.register
-    def _(self, kwb: KeywordBelief) -> AstExpression:
-        return AstLiteral("keyword_said", [AstString(kwb.keyword)])
-
-    @_astify.register
-    def _(self, sb: SemanticBelief) -> AstExpression:
-        return AstLiteral(self.slugify(sb))
-
-    @_astify.register
-    def _(self, ib: InferredBelief) -> AstExpression:
-        return AstBinaryOp(
-            self._astify(ib.left),
-            BinaryOperatorType.AND if ib.operator == LogicalOperator.AND else BinaryOperatorType.OR,
-            self._astify(ib.right),
-        )
-
-    @_astify.register
-    def _(self, norm: Norm) -> AstExpression:
-        functor = "critical_norm" if norm.critical else "norm"
-        return AstLiteral(functor, [AstString(norm.norm)])
-
-    @_astify.register
-    def _(self, phase: Phase) -> AstExpression:
-        return AstLiteral("phase", [AstString(str(phase.id))])
-
-    @_astify.register
-    def _(self, goal: Goal, achieved: bool = False) -> AstExpression:
-        return AstLiteral(f"{'achieved_' if achieved else ''}{self._slugify_str(goal.name)}")
-
-    @_astify.register
-    def _(self, trigger: Trigger) -> AstExpression:
-        return AstLiteral(self.slugify(trigger))
-
-    @_astify.register
-    def _(self, sa: SpeechAction) -> AstExpression:
-        return AstLiteral("say", [AstString(sa.text)])
-
-    @_astify.register
-    def _(self, ga: GestureAction) -> AstExpression:
-        gesture = ga.gesture
-        return AstLiteral("gesture", [AstString(gesture.type), AstString(gesture.name)])
-
-    @_astify.register
-    def _(self, la: LLMAction) -> AstExpression:
-        return AstLiteral("reply_with_goal", [AstString(la.goal)])
-
-    @singledispatchmethod
-    @staticmethod
-    def slugify(element: ProgramElement) -> str:
-        raise NotImplementedError(f"Cannot convert element {element} to a slug.")
-
-    @slugify.register
-    @staticmethod
-    def _(n: Norm) -> str:
-        return f"norm_{AgentSpeakGenerator._slugify_str(n.norm)}"
-
-    @slugify.register
-    @staticmethod
-    def _(sb: SemanticBelief) -> str:
-        return f"semantic_{AgentSpeakGenerator._slugify_str(sb.name)}"
-
-    @slugify.register
-    @staticmethod
-    def _(g: BaseGoal) -> str:
-        return AgentSpeakGenerator._slugify_str(g.name)
-
-    @slugify.register
-    @staticmethod
-    def _(t: Trigger):
-        return f"trigger_{AgentSpeakGenerator._slugify_str(t.name)}"
-
-    @staticmethod
-    def _slugify_str(text: str) -> str:
-        return slugify(text, separator="_", stopwords=["a", "an", "the", "we", "you", "I"])

src/control_backend/agents/bdi/asl_ast.py

@@ -0,0 +1,203 @@
+import typing
+from dataclasses import dataclass, field
+
+# --- Types ---
+
+
+@dataclass
+class BeliefLiteral:
+    """
+    Represents a literal or atom.
+    Example: phase(1), user_said("hello"), ~started
+    """
+
+    functor: str
+    args: list[str] = field(default_factory=list)
+    negated: bool = False
+
+    def __str__(self):
+        # In ASL, 'not' is usually for closed-world assumption (prolog style),
+        # '~' is for explicit negation in beliefs.
+        # For simplicity in behavior trees, we often use 'not' for conditions.
+        prefix = "not " if self.negated else ""
+        if not self.args:
+            return f"{prefix}{self.functor}"
+
+        # Clean args to ensure strings are quoted if they look like strings,
+        # but usually the converter handles the quoting of string literals.
+        args_str = ", ".join(self.args)
+        return f"{prefix}{self.functor}({args_str})"
+
+
+@dataclass
+class GoalLiteral:
+    name: str
+
+    def __str__(self):
+        return f"!{self.name}"
+
+
+@dataclass
+class ActionLiteral:
+    """
+    Represents a step in a plan body.
+    Example: .say("Hello") or !achieve_goal
+    """
+
+    code: str
+
+    def __str__(self):
+        return self.code
+
+
+@dataclass
+class BinaryOp:
+    """
+    Represents logical operations.
+    Example: (A & B) | C
+    """
+
+    left: "Expression | str"
+    operator: typing.Literal["&", "|"]
+    right: "Expression | str"
+
+    def __str__(self):
+        l_str = str(self.left)
+        r_str = str(self.right)
+
+        if isinstance(self.left, BinaryOp):
+            l_str = f"({l_str})"
+        if isinstance(self.right, BinaryOp):
+            r_str = f"({r_str})"
+
+        return f"{l_str} {self.operator} {r_str}"
+
+
+Literal = BeliefLiteral | GoalLiteral | ActionLiteral
+Expression = Literal | BinaryOp | str
+
+
+@dataclass
+class Rule:
+    """
+    Represents an inference rule.
+    Example: head :- body.
+    """
+
+    head: Expression
+    body: Expression | None = None
+
+    def __str__(self):
+        if not self.body:
+            return f"{self.head}."
+        return f"{self.head} :- {self.body}."
+
+
+@dataclass
+class PersistentRule:
+    """
+    Represents an inference rule, where the inferred belief is persistent when formed.
+    """
+
+    head: Expression
+    body: Expression
+
+    def __str__(self):
+        if not self.body:
+            raise Exception("Rule without body should not be persistent.")
+
+        lines = []
+
+        if isinstance(self.body, BinaryOp):
+            lines.append(f"+{self.body.left}")
+            if self.body.operator == "&":
+                lines.append(f"  :   {self.body.right}")
+            lines.append(f"  <-  +{self.head}.")
+            if self.body.operator == "|":
+                lines.append(f"+{self.body.right}")
+                lines.append(f"  <-  +{self.head}.")
+
+        return "\n".join(lines)
+
+
+@dataclass
+class Plan:
+    """
+    Represents a plan.
+    Syntax: +trigger : context <- body.
+    """
+
+    trigger: BeliefLiteral | GoalLiteral
+    context: list[Expression] = field(default_factory=list)
+    body: list[ActionLiteral] = field(default_factory=list)
+
+    def __str__(self):
+        # Indentation settings
+        INDENT = "      "
+        ARROW = "\n  <-  "
+        COLON = "\n  :   "
+
+        # Build Header
+        header = f"+{self.trigger}"
+        if self.context:
+            ctx_str = f" &\n{INDENT}".join(str(c) for c in self.context)
+            header += f"{COLON}{ctx_str}"
+
+        # Case 1: Empty body
+        if not self.body:
+            return f"{header}."
+
+        # Case 2: Short body (optional optimization, keeping it uniform usually better)
+        header += ARROW
+
+        lines = []
+        # We start the first action on the same line or next line.
+        # Let's put it on the next line for readability if there are multiple.
+
+        if len(self.body) == 1:
+            return f"{header}{self.body[0]}."
+
+        # First item
+        lines.append(f"{header}{self.body[0]};")
+        # Middle items
+        for item in self.body[1:-1]:
+            lines.append(f"{INDENT}{item};")
+        # Last item
+        lines.append(f"{INDENT}{self.body[-1]}.")
+
+        return "\n".join(lines)
+
+
+@dataclass
+class AgentSpeakFile:
+    """
+    Root element representing the entire generated file.
+    """
+
+    initial_beliefs: list[Rule] = field(default_factory=list)
+    inference_rules: list[Rule | PersistentRule] = field(default_factory=list)
+    plans: list[Plan] = field(default_factory=list)
+
+    def __str__(self):
+        sections = []
+
+        if self.initial_beliefs:
+            sections.append("// --- Initial Beliefs & Facts ---")
+            sections.extend(str(rule) for rule in self.initial_beliefs)
+            sections.append("")
+
+        if self.inference_rules:
+            sections.append("// --- Inference Rules ---")
+            sections.extend(str(rule) for rule in self.inference_rules if isinstance(rule, Rule))
+            sections.append("")
+            sections.extend(
+                str(rule) for rule in self.inference_rules if isinstance(rule, PersistentRule)
+            )
+            sections.append("")
+
+        if self.plans:
+            sections.append("// --- Plans ---")
+            # Separate plans by a newline for readability
+            sections.extend(str(plan) + "\n" for plan in self.plans)
+
+        return "\n".join(sections)

src/control_backend/agents/bdi/asl_gen.py

@@ -0,0 +1,425 @@
+import asyncio
+import time
+from functools import singledispatchmethod
+
+from slugify import slugify
+
+from control_backend.agents.bdi import BDICoreAgent
+from control_backend.agents.bdi.asl_ast import (
+    ActionLiteral,
+    AgentSpeakFile,
+    BeliefLiteral,
+    BinaryOp,
+    Expression,
+    GoalLiteral,
+    PersistentRule,
+    Plan,
+    Rule,
+)
+from control_backend.agents.bdi.bdi_program_manager import test_program
+from control_backend.schemas.program import (
+    BasicBelief,
+    Belief,
+    ConditionalNorm,
+    GestureAction,
+    Goal,
+    InferredBelief,
+    KeywordBelief,
+    LLMAction,
+    LogicalOperator,
+    Phase,
+    Program,
+    ProgramElement,
+    SemanticBelief,
+    SpeechAction,
+)
+
+
+async def do_things():
+    res = input("Wanna generate")
+    if res == "y":
+        program = AgentSpeakGenerator().generate(test_program)
+        filename = f"{int(time.time())}.asl"
+        with open(filename, "w") as f:
+            f.write(program)
+    else:
+        # filename = "0test.asl"
+        filename = "1766062491.asl"
+    bdi_agent = BDICoreAgent("BDICoreAgent", filename)
+    flag = asyncio.Event()
+    await bdi_agent.start()
+    await flag.wait()
+
+
+def do_other_things():
+    print(AgentSpeakGenerator().generate(test_program))
+
+
+class AgentSpeakGenerator:
+    """
+    Converts a Pydantic Program behavior model into an AgentSpeak(L) AST,
+    then renders it to a string.
+    """
+
+    def generate(self, program: Program) -> str:
+        asl = AgentSpeakFile()
+
+        self._generate_startup(program, asl)
+
+        for i, phase in enumerate(program.phases):
+            next_phase = program.phases[i + 1] if i < len(program.phases) - 1 else None
+
+            self._generate_phase_flow(phase, next_phase, asl)
+
+            self._generate_norms(phase, asl)
+
+            self._generate_goals(phase, asl)
+
+            self._generate_triggers(phase, asl)
+
+        self._generate_fallbacks(program, asl)
+
+        return str(asl)
+
+    # --- Section: Startup & Phase Management ---
+
+    def _generate_startup(self, program: Program, asl: AgentSpeakFile):
+        if not program.phases:
+            return
+
+        # Initial belief: phase(start).
+        asl.initial_beliefs.append(Rule(head=BeliefLiteral("phase", ['"start"'])))
+
+        # Startup plan: +started : phase(start) <- -phase(start); +phase(first_id).
+        asl.plans.append(
+            Plan(
+                trigger=BeliefLiteral("started"),
+                context=[BeliefLiteral("phase", ['"start"'])],
+                body=[
+                    ActionLiteral('-phase("start")'),
+                    ActionLiteral(f'+phase("{program.phases[0].id}")'),
+                ],
+            )
+        )
+
+        # Initial plans:
+        asl.plans.append(
+            Plan(
+                trigger=GoalLiteral("generate_response_with_goal(Goal)"),
+                context=[BeliefLiteral("user_said", ["Message"])],
+                body=[
+                    ActionLiteral("+responded_this_turn"),
+                    ActionLiteral(".findall(Norm, norm(Norm), Norms)"),
+                    ActionLiteral(".reply_with_goal(Message, Norms, Goal)"),
+                ],
+            )
+        )
+
+    def _generate_phase_flow(self, phase: Phase, next_phase: Phase | None, asl: AgentSpeakFile):
+        """Generates the main loop listener and the transition logic for this phase."""
+
+        # +user_said(Message) : phase(ID) <- !goal1; !goal2; !transition_phase.
+        goal_actions = [ActionLiteral("-responded_this_turn")]
+        goal_actions += [
+            ActionLiteral(f"!check_{self._slugify_str(keyword)}")
+            for keyword in self._get_keyword_conditionals(phase)
+        ]
+        goal_actions += [ActionLiteral(f"!{self._slugify(g)}") for g in phase.goals]
+        goal_actions.append(ActionLiteral("!transition_phase"))
+
+        asl.plans.append(
+            Plan(
+                trigger=BeliefLiteral("user_said", ["Message"]),
+                context=[BeliefLiteral("phase", [f'"{phase.id}"'])],
+                body=goal_actions,
+            )
+        )
+
+        # +!transition_phase : phase(ID) <- -phase(ID); +(NEXT_ID).
+        next_id = str(next_phase.id) if next_phase else "end"
+
+        transition_context = [BeliefLiteral("phase", [f'"{phase.id}"'])]
+        if phase.goals:
+            transition_context.append(BeliefLiteral(f"achieved_{self._slugify(phase.goals[-1])}"))
+
+        asl.plans.append(
+            Plan(
+                trigger=GoalLiteral("transition_phase"),
+                context=transition_context,
+                body=[
+                    ActionLiteral(f'-phase("{phase.id}")'),
+                    ActionLiteral(f'+phase("{next_id}")'),
+                    ActionLiteral("user_said(Anything)"),
+                    ActionLiteral("-+user_said(Anything)"),
+                ],
+            )
+        )
+
+    def _get_keyword_conditionals(self, phase: Phase) -> list[str]:
+        res = []
+        for belief in self._extract_basic_beliefs_from_phase(phase):
+            if isinstance(belief, KeywordBelief):
+                res.append(belief.keyword)
+
+        return res
+
+    # --- Section: Norms & Beliefs ---
+
+    def _generate_norms(self, phase: Phase, asl: AgentSpeakFile):
+        for norm in phase.norms:
+            norm_slug = f'"{norm.norm}"'
+            head = BeliefLiteral("norm", [norm_slug])
+
+            # Base context is the phase
+            phase_lit = BeliefLiteral("phase", [f'"{phase.id}"'])
+
+            if isinstance(norm, ConditionalNorm):
+                self._ensure_belief_inference(norm.condition, asl)
+
+                condition_expr = self._belief_to_expr(norm.condition)
+                body = BinaryOp(phase_lit, "&", condition_expr)
+            else:
+                body = phase_lit
+
+            asl.inference_rules.append(Rule(head=head, body=body))
+
+    def _ensure_belief_inference(self, belief: Belief, asl: AgentSpeakFile):
+        """
+        Recursively adds rules to infer beliefs.
+        Checks strictly to avoid duplicates if necessary,
+        though ASL engines often handle redefinition or we can use a set to track processed IDs.
+        """
+        if isinstance(belief, KeywordBelief):
+            pass
+            # # Rule: keyword_said("word") :- user_said(M) & .substring("word", M, P) & P >= 0.
+            # kwd_slug = f'"{belief.keyword}"'
+            # head = BeliefLiteral("keyword_said", [kwd_slug])
+            #
+            # # Avoid duplicates
+            # if any(str(r.head) == str(head) for r in asl.inference_rules):
+            #     return
+            #
+            # body = BinaryOp(
+            #     BeliefLiteral("user_said", ["Message"]),
+            #     "&",
+            #     BinaryOp(f".substring({kwd_slug}, Message, Pos)", "&", "Pos >= 0"),
+            # )
+            #
+            # asl.inference_rules.append(Rule(head=head, body=body))
+
+        elif isinstance(belief, InferredBelief):
+            self._ensure_belief_inference(belief.left, asl)
+            self._ensure_belief_inference(belief.right, asl)
+
+            slug = self._slugify(belief)
+            head = BeliefLiteral(slug)
+
+            if any(str(r.head) == str(head) for r in asl.inference_rules):
+                return
+
+            op_char = "&" if belief.operator == LogicalOperator.AND else "|"
+            body = BinaryOp(
+                self._belief_to_expr(belief.left), op_char, self._belief_to_expr(belief.right)
+            )
+            asl.inference_rules.append(PersistentRule(head=head, body=body))
+
+    def _belief_to_expr(self, belief: Belief) -> Expression:
+        if isinstance(belief, KeywordBelief):
+            return BeliefLiteral("keyword_said", [f'"{belief.keyword}"'])
+        else:
+            return BeliefLiteral(self._slugify(belief))
+
+    # --- Section: Goals ---
+
+    def _generate_goals(self, phase: Phase, asl: AgentSpeakFile):
+        previous_goal: Goal | None = None
+        for goal in phase.goals:
+            self._generate_goal_plan_recursive(goal, str(phase.id), previous_goal, asl)
+            previous_goal = goal
+
+    def _generate_goal_plan_recursive(
+        self,
+        goal: Goal,
+        phase_id: str,
+        previous_goal: Goal | None,
+        asl: AgentSpeakFile,
+        responded_needed: bool = True,
+        can_fail: bool = True,
+    ):
+        goal_slug = self._slugify(goal)
+
+        # phase(ID) & not responded_this_turn & not achieved_goal
+        context = [
+            BeliefLiteral("phase", [f'"{phase_id}"']),
+        ]
+
+        if responded_needed:
+            context.append(BeliefLiteral("responded_this_turn", negated=True))
+        if can_fail:
+            context.append(BeliefLiteral(f"achieved_{goal_slug}", negated=True))
+
+        if previous_goal:
+            prev_slug = self._slugify(previous_goal)
+            context.append(BeliefLiteral(f"achieved_{prev_slug}"))
+
+        body_actions = []
+        sub_goals_to_process = []
+
+        for step in goal.plan.steps:
+            if isinstance(step, Goal):
+                sub_slug = self._slugify(step)
+                body_actions.append(ActionLiteral(f"!{sub_slug}"))
+                sub_goals_to_process.append(step)
+            elif isinstance(step, SpeechAction):
+                body_actions.append(ActionLiteral(f'.say("{step.text}")'))
+            elif isinstance(step, GestureAction):
+                body_actions.append(ActionLiteral(f'.gesture("{step.gesture}")'))
+            elif isinstance(step, LLMAction):
+                body_actions.append(ActionLiteral(f'!generate_response_with_goal("{step.goal}")'))
+
+        # Mark achievement
+        if not goal.can_fail:
+            body_actions.append(ActionLiteral(f"+achieved_{goal_slug}"))
+
+        asl.plans.append(Plan(trigger=GoalLiteral(goal_slug), context=context, body=body_actions))
+        asl.plans.append(
+            Plan(trigger=GoalLiteral(goal_slug), context=[], body=[ActionLiteral("true")])
+        )
+
+        prev_sub = None
+        for sub_goal in sub_goals_to_process:
+            self._generate_goal_plan_recursive(sub_goal, phase_id, prev_sub, asl)
+            prev_sub = sub_goal
+
+    # --- Section: Triggers ---
+
+    def _generate_triggers(self, phase: Phase, asl: AgentSpeakFile):
+        for keyword in self._get_keyword_conditionals(phase):
+            asl.plans.append(
+                Plan(
+                    trigger=GoalLiteral(f"check_{self._slugify_str(keyword)}"),
+                    context=[
+                        ActionLiteral(
+                            f'user_said(Message) & .substring("{keyword}", Message, Pos) & Pos >= 0'
+                        )
+                    ],
+                    body=[
+                        ActionLiteral(f'+keyword_said("{keyword}")'),
+                        ActionLiteral(f'-keyword_said("{keyword}")'),
+                    ],
+                )
+            )
+            asl.plans.append(
+                Plan(
+                    trigger=GoalLiteral(f"check_{self._slugify_str(keyword)}"),
+                    body=[ActionLiteral("true")],
+                )
+            )
+
+        for trigger in phase.triggers:
+            self._ensure_belief_inference(trigger.condition, asl)
+
+            trigger_belief_slug = self._belief_to_expr(trigger.condition)
+
+            body_actions = []
+            sub_goals = []
+
+            for step in trigger.plan.steps:
+                if isinstance(step, Goal):
+                    sub_slug = self._slugify(step)
+                    body_actions.append(ActionLiteral(f"!{sub_slug}"))
+                    sub_goals.append(step)
+                elif isinstance(step, SpeechAction):
+                    body_actions.append(ActionLiteral(f'.say("{step.text}")'))
+                elif isinstance(step, GestureAction):
+                    body_actions.append(
+                        ActionLiteral(f'.gesture("{step.gesture.type}", "{step.gesture.name}")')
+                    )
+                elif isinstance(step, LLMAction):
+                    body_actions.append(
+                        ActionLiteral(f'!generate_response_with_goal("{step.goal}")')
+                    )
+
+            asl.plans.append(
+                Plan(
+                    trigger=BeliefLiteral(trigger_belief_slug),
+                    context=[BeliefLiteral("phase", [f'"{phase.id}"'])],
+                    body=body_actions,
+                )
+            )
+
+            # Recurse for triggered goals
+            prev_sub = None
+            for sub_goal in sub_goals:
+                self._generate_goal_plan_recursive(
+                    sub_goal, str(phase.id), prev_sub, asl, False, False
+                )
+                prev_sub = sub_goal
+
+    # --- Section: Fallbacks ---
+
+    def _generate_fallbacks(self, program: Program, asl: AgentSpeakFile):
+        asl.plans.append(
+            Plan(trigger=GoalLiteral("transition_phase"), context=[], body=[ActionLiteral("true")])
+        )
+
+    # --- Helpers ---
+
+    @singledispatchmethod
+    def _slugify(self, element: ProgramElement) -> str:
+        if element.name:
+            raise NotImplementedError("Cannot slugify this element.")
+        return self._slugify_str(element.name)
+
+    @_slugify.register
+    def _(self, goal: Goal) -> str:
+        if goal.name:
+            return self._slugify_str(goal.name)
+        return f"goal_{goal.id.hex}"
+
+    @_slugify.register
+    def _(self, kwb: KeywordBelief) -> str:
+        return f"keyword_said({kwb.keyword})"
+
+    @_slugify.register
+    def _(self, sb: SemanticBelief) -> str:
+        return self._slugify_str(sb.description)
+
+    @_slugify.register
+    def _(self, ib: InferredBelief) -> str:
+        return self._slugify_str(ib.name)
+
+    def _slugify_str(self, text: str) -> str:
+        return slugify(text, separator="_", stopwords=["a", "an", "the", "we", "you", "I"])
+
+    def _extract_basic_beliefs_from_program(self, program: Program) -> list[BasicBelief]:
+        beliefs = []
+
+        for phase in program.phases:
+            beliefs.extend(self._extract_basic_beliefs_from_phase(phase))
+
+        return beliefs
+
+    def _extract_basic_beliefs_from_phase(self, phase: Phase) -> list[BasicBelief]:
+        beliefs = []
+
+        for norm in phase.norms:
+            if isinstance(norm, ConditionalNorm):
+                beliefs += self._extract_basic_beliefs_from_belief(norm.condition)
+
+        for trigger in phase.triggers:
+            beliefs += self._extract_basic_beliefs_from_belief(trigger.condition)
+
+        return beliefs
+
+    def _extract_basic_beliefs_from_belief(self, belief: Belief) -> list[BasicBelief]:
+        if isinstance(belief, InferredBelief):
+            return self._extract_basic_beliefs_from_belief(
+                belief.left
+            ) + self._extract_basic_beliefs_from_belief(belief.right)
+        return [belief]
+
+
+if __name__ == "__main__":
+    asyncio.run(do_things())
+    # do_other_things()y

src/control_backend/agents/bdi/astv2.py

@@ -187,10 +187,9 @@ class StatementType(StrEnum):
     EMPTY = ""
     DO_ACTION = "."
     ACHIEVE_GOAL = "!"
-    TEST_GOAL = "?"
+    # TEST_GOAL = "?"  # TODO
     ADD_BELIEF = "+"
     REMOVE_BELIEF = "-"
-    REPLACE_BELIEF = "-+"
 
 
 @dataclass

src/control_backend/agents/bdi/bdi_core_agent.py

@@ -1,6 +1,5 @@
 import asyncio
 import copy
-import json
 import time
 from collections.abc import Iterable
 
@@ -14,7 +13,7 @@ from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
 from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.llm_prompt_message import LLMPromptMessage
-from control_backend.schemas.ri_message import GestureCommand, RIEndpoint, SpeechCommand
+from control_backend.schemas.ri_message import SpeechCommand
 
 DELIMITER = ";\n"  # TODO: temporary until we support lists in AgentSpeak
 
@@ -43,13 +42,13 @@ class BDICoreAgent(BaseAgent):
 
     bdi_agent: agentspeak.runtime.Agent
 
-    def __init__(self, name: str):
+    def __init__(self, name: str, asl: str):
         super().__init__(name)
+        self.asl_file = asl
         self.env = agentspeak.runtime.Environment()
         # Deep copy because we don't actually want to modify the standard actions globally
         self.actions = copy.deepcopy(agentspeak.stdlib.actions)
         self._wake_bdi_loop = asyncio.Event()
-        self._bdi_loop_task = None
 
     async def setup(self) -> None:
         """
@@ -66,22 +65,19 @@ class BDICoreAgent(BaseAgent):
         await self._load_asl()
 
         # Start the BDI cycle loop
-        self._bdi_loop_task = self.add_behavior(self._bdi_loop())
+        self.add_behavior(self._bdi_loop())
         self._wake_bdi_loop.set()
         self.logger.debug("Setup complete.")
 
-    async def _load_asl(self, file_name: str | None = None) -> None:
+    async def _load_asl(self):
         """
         Load and parse the AgentSpeak source file.
         """
-        file_name = file_name or "src/control_backend/agents/bdi/default_behavior.asl"
-
         try:
-            with open(file_name) as source:
+            with open(self.asl_file) as source:
                 self.bdi_agent = self.env.build_agent(source, self.actions)
-                self.logger.info(f"Loaded new ASL from {file_name}.")
         except FileNotFoundError:
-            self.logger.warning(f"Could not find the specified ASL file at {file_name}.")
+            self.logger.warning(f"Could not find the specified ASL file at {self.asl_file}.")
             self.bdi_agent = agentspeak.runtime.Agent(self.env, self.name)
 
     async def _bdi_loop(self):
@@ -101,12 +97,14 @@ class BDICoreAgent(BaseAgent):
             maybe_more_work = True
             while maybe_more_work:
                 maybe_more_work = False
+                self.logger.debug("Stepping BDI.")
                 if self.bdi_agent.step():
                     maybe_more_work = True
 
                 if not maybe_more_work:
                     deadline = self.bdi_agent.shortest_deadline()
                     if deadline:
+                        self.logger.debug("Sleeping until %s", deadline)
                         await asyncio.sleep(deadline - time.time())
                         maybe_more_work = True
                     else:
@@ -118,7 +116,6 @@ class BDICoreAgent(BaseAgent):
         Handle incoming messages.
 
         - **Beliefs**: Updates the internal belief base.
-        - **Program**: Updates the internal agentspeak file to match the current program.
         - **LLM Responses**: Forwards the generated text to the Robot Speech Agent (actuation).
 
         :param msg: The received internal message.
@@ -133,13 +130,6 @@ class BDICoreAgent(BaseAgent):
                 self.logger.exception("Error processing belief.")
             return
 
-        # New agentspeak file
-        if msg.thread == "new_program":
-            if self._bdi_loop_task:
-                self._bdi_loop_task.cancel()
-            await self._load_asl(msg.body)
-            self.add_behavior(self._bdi_loop())
-
         # The message was not a belief, handle special cases based on sender
         match msg.sender:
             case settings.agent_settings.llm_name:
@@ -154,20 +144,6 @@ class BDICoreAgent(BaseAgent):
                     body=cmd.model_dump_json(),
                 )
                 await self.send(out_msg)
-            case settings.agent_settings.user_interrupt_name:
-                self.logger.debug("Received user interruption: %s", msg)
-
-                match msg.thread:
-                    case "force_phase_transition":
-                        self._set_goal("transition_phase")
-                    case "force_trigger":
-                        self._force_trigger(msg.body)
-                    case "force_norm":
-                        self._force_norm(msg.body)
-                    case "force_next_phase":
-                        self._force_next_phase()
-                    case _:
-                        self.logger.warning("Received unknow user interruption: %s", msg)
 
     def _apply_belief_changes(self, belief_changes: BeliefMessage):
         """
@@ -214,33 +190,14 @@ class BDICoreAgent(BaseAgent):
             agentspeak.runtime.Intention(),
         )
 
-        # Check for transitions
-        self.bdi_agent.call(
-            agentspeak.Trigger.addition,
-            agentspeak.GoalType.achievement,
-            agentspeak.Literal("transition_phase"),
-            agentspeak.runtime.Intention(),
-        )
-
-        # Check triggers
-        self.bdi_agent.call(
-            agentspeak.Trigger.addition,
-            agentspeak.GoalType.achievement,
-            agentspeak.Literal("check_triggers"),
-            agentspeak.runtime.Intention(),
-        )
-
         self._wake_bdi_loop.set()
 
         self.logger.debug(f"Added belief {self.format_belief_string(name, args)}")
 
-    def _remove_belief(self, name: str, args: Iterable[str] | None):
+    def _remove_belief(self, name: str, args: Iterable[str]):
         """
         Removes a specific belief (with arguments), if it exists.
         """
-        if args is None:
-            term = agentspeak.Literal(name)
-        else:
         new_args = (agentspeak.Literal(arg) for arg in args)
         term = agentspeak.Literal(name, new_args)
 
@@ -282,43 +239,6 @@ class BDICoreAgent(BaseAgent):
 
         self.logger.debug(f"Removed {removed_count} beliefs.")
 
-    def _set_goal(self, name: str, args: Iterable[str] | None = None):
-        args = args or []
-
-        if args:
-            merged_args = DELIMITER.join(arg for arg in args)
-            new_args = (agentspeak.Literal(merged_args),)
-            term = agentspeak.Literal(name, new_args)
-        else:
-            term = agentspeak.Literal(name)
-
-        self.bdi_agent.call(
-            agentspeak.Trigger.addition,
-            agentspeak.GoalType.achievement,
-            term,
-            agentspeak.runtime.Intention(),
-        )
-
-        self._wake_bdi_loop.set()
-
-        self.logger.debug(f"Set goal !{self.format_belief_string(name, args)}.")
-
-    def _force_trigger(self, name: str):
-        self._set_goal(name)
-
-        self.logger.info("Manually forced trigger %s.", name)
-
-    # TODO: make this compatible for critical norms
-    def _force_norm(self, name: str):
-        self._add_belief(f"force_{name}")
-
-        self.logger.info("Manually forced norm %s.", name)
-
-    def _force_next_phase(self):
-        self._set_goal("force_transition_phase")
-
-        self.logger.info("Manually forced phase transition.")
-
     def _add_custom_actions(self) -> None:
         """
         Add any custom actions here. Inside `@self.actions.add()`, the first argument is
@@ -326,15 +246,20 @@ class BDICoreAgent(BaseAgent):
         the function expects (which will be located in `term.args`).
         """
 
-        @self.actions.add(".reply", 2)
+        @self.actions.add(".reply", 3)
-        def _reply(agent, term, intention):
+        def _reply(agent: "BDICoreAgent", term, intention):
             """
             Let the LLM generate a response to a user's utterance with the current norms and goals.
             """
             message_text = agentspeak.grounded(term.args[0], intention.scope)
             norms = agentspeak.grounded(term.args[1], intention.scope)
+            goals = agentspeak.grounded(term.args[2], intention.scope)
 
-            self.add_behavior(self._send_to_llm(str(message_text), str(norms), ""))
+            self.logger.debug("Norms: %s", norms)
+            self.logger.debug("Goals: %s", goals)
+            self.logger.debug("User text: %s", message_text)
+
+            asyncio.create_task(self._send_to_llm(str(message_text), str(norms), str(goals)))
             yield
 
         @self.actions.add(".reply_with_goal", 3)
@@ -346,24 +271,18 @@ class BDICoreAgent(BaseAgent):
             message_text = agentspeak.grounded(term.args[0], intention.scope)
             norms = agentspeak.grounded(term.args[1], intention.scope)
             goal = agentspeak.grounded(term.args[2], intention.scope)
-            self.add_behavior(self._send_to_llm(str(message_text), str(norms), str(goal)))
-            yield
 
-        @self.actions.add(".notify_norms", 1)
+            self.logger.debug(
-        def _notify_norms(agent, term, intention):
+                '"reply_with_goal" action called with message=%s, norms=%s, goal=%s',
-            norms = agentspeak.grounded(term.args[0], intention.scope)
+                message_text,
-
+                norms,
-            norm_update_message = InternalMessage(
+                goal,
-                to=settings.agent_settings.user_interrupt_name,
-                thread="active_norms_update",
-                body=str(norms),
             )
-
+            # asyncio.create_task(self._send_to_llm(str(message_text), norms, str(goal)))
-            self.add_behavior(self.send(norm_update_message, should_log=False))
             yield
 
         @self.actions.add(".say", 1)
-        def _say(agent, term, intention):
+        def _say(agent: "BDICoreAgent", term, intention):
             """
             Make the robot say the given text instantly.
             """
@@ -371,27 +290,17 @@ class BDICoreAgent(BaseAgent):
 
             self.logger.debug('"say" action called with text=%s', message_text)
 
-            speech_command = SpeechCommand(data=message_text)
+            # speech_command = SpeechCommand(data=message_text)
-            speech_message = InternalMessage(
+            # speech_message = InternalMessage(
-                to=settings.agent_settings.robot_speech_name,
+            #     to=settings.agent_settings.robot_speech_name,
-                sender=settings.agent_settings.bdi_core_name,
+            #     sender=settings.agent_settings.bdi_core_name,
-                body=speech_command.model_dump_json(),
+            #     body=speech_command.model_dump_json(),
-            )
+            # )
-
+            # asyncio.create_task(agent.send(speech_message))
-            self.add_behavior(self.send(speech_message))
-
-            chat_history_message = InternalMessage(
-                to=settings.agent_settings.llm_name,
-                thread="assistant_message",
-                body=str(message_text),
-            )
-
-            self.add_behavior(self.send(chat_history_message))
-
             yield
 
         @self.actions.add(".gesture", 2)
-        def _gesture(agent, term, intention):
+        def _gesture(agent: "BDICoreAgent", term, intention):
             """
             Make the robot perform the given gesture instantly.
             """
@@ -404,118 +313,15 @@ class BDICoreAgent(BaseAgent):
                 gesture_name,
             )
 
-            if str(gesture_type) == "single":
+            # gesture = Gesture(type=gesture_type, name=gesture_name)
-                endpoint = RIEndpoint.GESTURE_SINGLE
+            # gesture_message = InternalMessage(
-            elif str(gesture_type) == "tag":
+            #     to=settings.agent_settings.robot_gesture_name,
-                endpoint = RIEndpoint.GESTURE_TAG
+            #     sender=settings.agent_settings.bdi_core_name,
-            else:
+            #     body=gesture.model_dump_json(),
-                self.logger.warning("Gesture type %s could not be resolved.", gesture_type)
+            # )
-                endpoint = RIEndpoint.GESTURE_SINGLE
+            # asyncio.create_task(agent.send(gesture_message))
-
-            gesture_command = GestureCommand(endpoint=endpoint, data=gesture_name)
-            gesture_message = InternalMessage(
-                to=settings.agent_settings.robot_gesture_name,
-                sender=settings.agent_settings.bdi_core_name,
-                body=gesture_command.model_dump_json(),
-            )
-            self.add_behavior(self.send(gesture_message))
             yield
 
-        @self.actions.add(".notify_user_said", 1)
-        def _notify_user_said(agent, term, intention):
-            user_said = agentspeak.grounded(term.args[0], intention.scope)
-
-            msg = InternalMessage(
-                to=settings.agent_settings.llm_name, thread="user_message", body=str(user_said)
-            )
-
-            self.add_behavior(self.send(msg))
-
-            yield
-
-        @self.actions.add(".notify_trigger_start", 1)
-        def _notify_trigger_start(agent, term, intention):
-            """
-            Notify the UI about the trigger we just started doing.
-            """
-            trigger_name = agentspeak.grounded(term.args[0], intention.scope)
-
-            self.logger.debug("Started trigger %s", trigger_name)
-
-            msg = InternalMessage(
-                to=settings.agent_settings.user_interrupt_name,
-                sender=self.name,
-                thread="trigger_start",
-                body=str(trigger_name),
-            )
-
-            # TODO: check with Pim
-            self.add_behavior(self.send(msg))
-
-            yield
-
-        @self.actions.add(".notify_trigger_end", 1)
-        def _notify_trigger_end(agent, term, intention):
-            """
-            Notify the UI about the trigger we just started doing.
-            """
-            trigger_name = agentspeak.grounded(term.args[0], intention.scope)
-
-            self.logger.debug("Finished trigger %s", trigger_name)
-
-            msg = InternalMessage(
-                to=settings.agent_settings.user_interrupt_name,
-                sender=self.name,
-                thread="trigger_end",
-                body=str(trigger_name),
-            )
-
-            self.add_behavior(self.send(msg))
-
-            yield
-
-        @self.actions.add(".notify_goal_start", 1)
-        def _notify_goal_start(agent, term, intention):
-            """
-            Notify the UI about the goal we just started chasing.
-            """
-            goal_name = agentspeak.grounded(term.args[0], intention.scope)
-
-            self.logger.debug("Started chasing goal %s", goal_name)
-
-            msg = InternalMessage(
-                to=settings.agent_settings.user_interrupt_name,
-                sender=self.name,
-                thread="goal_start",
-                body=str(goal_name),
-            )
-
-            self.add_behavior(self.send(msg))
-
-            yield
-
-        @self.actions.add(".notify_transition_phase", 2)
-        def _notify_transition_phase(agent, term, intention):
-            """
-            Notify the BDI program manager about a phase transition.
-            """
-            old = agentspeak.grounded(term.args[0], intention.scope)
-            new = agentspeak.grounded(term.args[1], intention.scope)
-
-            msg = InternalMessage(
-                to=settings.agent_settings.bdi_program_manager_name,
-                thread="transition_phase",
-                body=json.dumps({"old": str(old), "new": str(new)}),
-            )
-
-            self.add_behavior(self.send(msg))
-
-            yield
-
-        @self.actions.add(".notify_ui", 0)
-        def _notify_ui(agent, term, intention):
-            pass
-
     async def _send_to_llm(self, text: str, norms: str, goals: str):
         """
         Sends a text query to the LLM agent asynchronously.
@@ -525,14 +331,13 @@ class BDICoreAgent(BaseAgent):
             to=settings.agent_settings.llm_name,
             sender=self.name,
             body=prompt.model_dump_json(),
-            thread="prompt_message",
         )
         await self.send(msg)
         self.logger.info("Message sent to LLM agent: %s", text)
 
     @staticmethod
-    def format_belief_string(name: str, args: Iterable[str] | None = []):
+    def format_belief_string(name: str, args: Iterable[str] = []):
         """
         Given a belief's name and its args, return a string of the form "name(*args)"
         """
-        return f"{name}{'(' if args else ''}{','.join(args or [])}{')' if args else ''}"
+        return f"{name}{'(' if args else ''}{','.join(args)}{')' if args else ''}"

src/control_backend/agents/bdi/bdi_program_manager.py

@@ -1,24 +1,599 @@
-import asyncio
+import uuid
-import json
+from collections.abc import Iterable
 
 import zmq
 from pydantic import ValidationError
+from slugify import slugify
 from zmq.asyncio import Context
 
 from control_backend.agents import BaseAgent
-from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.config import settings
-from control_backend.schemas.belief_list import BeliefList, GoalList
-from control_backend.schemas.internal_message import InternalMessage
 from control_backend.schemas.program import (
+    Action,
+    BasicBelief,
+    BasicNorm,
     Belief,
     ConditionalNorm,
+    GestureAction,
     Goal,
     InferredBelief,
+    KeywordBelief,
+    LLMAction,
+    LogicalOperator,
     Phase,
+    Plan,
     Program,
+    ProgramElement,
+    SemanticBelief,
+    SpeechAction,
+    Trigger,
 )
 
+test_program = Program(
+    phases=[
+        Phase(
+            norms=[
+                BasicNorm(norm="Talk like a pirate", id=uuid.uuid4()),
+                ConditionalNorm(
+                    condition=InferredBelief(
+                        left=KeywordBelief(keyword="Arr", id=uuid.uuid4()),
+                        right=SemanticBelief(
+                            description="testing", name="semantic belief", id=uuid.uuid4()
+                        ),
+                        operator=LogicalOperator.OR,
+                        name="Talking to a pirate",
+                        id=uuid.uuid4(),
+                    ),
+                    norm="Use nautical terms",
+                    id=uuid.uuid4(),
+                ),
+                ConditionalNorm(
+                    condition=SemanticBelief(
+                        description="We are talking to a child",
+                        name="talking to child",
+                        id=uuid.uuid4(),
+                    ),
+                    norm="Do not use cuss words",
+                    id=uuid.uuid4(),
+                ),
+            ],
+            triggers=[
+                Trigger(
+                    condition=InferredBelief(
+                        left=KeywordBelief(keyword="key", id=uuid.uuid4()),
+                        right=InferredBelief(
+                            left=KeywordBelief(keyword="key2", id=uuid.uuid4()),
+                            right=SemanticBelief(
+                                description="Decode this", name="semantic belief 2", id=uuid.uuid4()
+                            ),
+                            operator=LogicalOperator.OR,
+                            name="test trigger inferred inner",
+                            id=uuid.uuid4(),
+                        ),
+                        operator=LogicalOperator.OR,
+                        name="test trigger inferred outer",
+                        id=uuid.uuid4(),
+                    ),
+                    plan=Plan(
+                        steps=[
+                            SpeechAction(text="Testing trigger", id=uuid.uuid4()),
+                            Goal(
+                                name="Testing trigger",
+                                plan=Plan(
+                                    steps=[LLMAction(goal="Do something", id=uuid.uuid4())],
+                                    id=uuid.uuid4(),
+                                ),
+                                id=uuid.uuid4(),
+                            ),
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                )
+            ],
+            goals=[
+                Goal(
+                    name="Determine user age",
+                    plan=Plan(
+                        steps=[LLMAction(goal="Determine the age of the user.", id=uuid.uuid4())],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+                Goal(
+                    name="Find the user's name",
+                    plan=Plan(
+                        steps=[
+                            Goal(
+                                name="Greet the user",
+                                plan=Plan(
+                                    steps=[LLMAction(goal="Greet the user.", id=uuid.uuid4())],
+                                    id=uuid.uuid4(),
+                                ),
+                                can_fail=False,
+                                id=uuid.uuid4(),
+                            ),
+                            Goal(
+                                name="Ask for name",
+                                plan=Plan(
+                                    steps=[
+                                        LLMAction(goal="Obtain the user's name.", id=uuid.uuid4())
+                                    ],
+                                    id=uuid.uuid4(),
+                                ),
+                                id=uuid.uuid4(),
+                            ),
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+                Goal(
+                    name="Tell a joke",
+                    plan=Plan(
+                        steps=[LLMAction(goal="Tell a joke.", id=uuid.uuid4())], id=uuid.uuid4()
+                    ),
+                    id=uuid.uuid4(),
+                ),
+            ],
+            id=uuid.uuid4(),
+        ),
+        Phase(
+            id=uuid.uuid4(),
+            norms=[
+                BasicNorm(norm="Use very gentle speech.", id=uuid.uuid4()),
+                ConditionalNorm(
+                    condition=SemanticBelief(
+                        description="We are talking to a child",
+                        name="talking to child",
+                        id=uuid.uuid4(),
+                    ),
+                    norm="Do not use cuss words",
+                    id=uuid.uuid4(),
+                ),
+            ],
+            triggers=[
+                Trigger(
+                    condition=InferredBelief(
+                        left=KeywordBelief(keyword="help", id=uuid.uuid4()),
+                        right=SemanticBelief(
+                            description="User is stuck", name="stuck", id=uuid.uuid4()
+                        ),
+                        operator=LogicalOperator.OR,
+                        name="help_or_stuck",
+                        id=uuid.uuid4(),
+                    ),
+                    plan=Plan(
+                        steps=[
+                            Goal(
+                                name="Unblock user",
+                                plan=Plan(
+                                    steps=[
+                                        LLMAction(
+                                            goal="Provide a step-by-step path to "
+                                            "resolve the user's issue.",
+                                            id=uuid.uuid4(),
+                                        )
+                                    ],
+                                    id=uuid.uuid4(),
+                                ),
+                                id=uuid.uuid4(),
+                            ),
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+            ],
+            goals=[
+                Goal(
+                    name="Clarify intent",
+                    plan=Plan(
+                        steps=[
+                            LLMAction(
+                                goal="Ask 1-2 targeted questions to clarify the "
+                                "user's intent, then proceed.",
+                                id=uuid.uuid4(),
+                            )
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+                Goal(
+                    name="Provide solution",
+                    plan=Plan(
+                        steps=[
+                            LLMAction(
+                                goal="Deliver a solution to complete the user's goal.",
+                                id=uuid.uuid4(),
+                            )
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+                Goal(
+                    name="Summarize next steps",
+                    plan=Plan(
+                        steps=[
+                            LLMAction(
+                                goal="Summarize what the user should do next.", id=uuid.uuid4()
+                            )
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+            ],
+        ),
+    ]
+)
+
+
+def do_things():
+    print(AgentSpeakGenerator().generate(test_program))
+
+
+class AgentSpeakGenerator:
+    """
+    Converts Pydantic representation of behavior programs into AgentSpeak(L) code string.
+    """
+
+    arrow_prefix = f"{' ' * 2}<-{' ' * 2}"
+    colon_prefix = f"{' ' * 2}:{' ' * 3}"
+    indent_prefix = " " * 6
+
+    def generate(self, program: Program) -> str:
+        lines = []
+        lines.append("")
+
+        lines += self._generate_initial_beliefs(program)
+
+        lines += self._generate_basic_flow(program)
+
+        lines += self._generate_phase_transitions(program)
+
+        lines += self._generate_norms(program)
+
+        lines += self._generate_belief_inference(program)
+
+        lines += self._generate_goals(program)
+
+        lines += self._generate_triggers(program)
+
+        return "\n".join(lines)
+
+    def _generate_initial_beliefs(self, program: Program) -> Iterable[str]:
+        yield "// --- Initial beliefs and agent startup ---"
+
+        yield "phase(start)."
+
+        yield ""
+
+        yield "+started"
+        yield f"{self.colon_prefix}phase(start)"
+        yield f"{self.arrow_prefix}phase({program.phases[0].id if program.phases else 'end'})."
+
+        yield from ["", ""]
+
+    def _generate_basic_flow(self, program: Program) -> Iterable[str]:
+        yield "// --- Basic flow ---"
+
+        for phase in program.phases:
+            yield from self._generate_basic_flow_per_phase(phase)
+
+        yield from ["", ""]
+
+    def _generate_basic_flow_per_phase(self, phase: Phase) -> Iterable[str]:
+        yield "+user_said(Message)"
+        yield f"{self.colon_prefix}phase({phase.id})"
+
+        goals = phase.goals
+        if goals:
+            yield f"{self.arrow_prefix}{self._slugify(goals[0], include_prefix=True)}"
+            for goal in goals[1:]:
+                yield f"{self.indent_prefix}{self._slugify(goal, include_prefix=True)}"
+
+        yield f"{self.indent_prefix if goals else self.arrow_prefix}!transition_phase."
+
+    def _generate_phase_transitions(self, program: Program) -> Iterable[str]:
+        yield "// --- Phase transitions ---"
+
+        if len(program.phases) == 0:
+            yield from ["", ""]
+            return
+
+        # TODO: remove outdated things
+
+        for i in range(-1, len(program.phases)):
+            predecessor = program.phases[i] if i >= 0 else None
+            successor = program.phases[i + 1] if i < len(program.phases) - 1 else None
+            yield from self._generate_phase_transition(predecessor, successor)
+
+        yield from self._generate_phase_transition(None, None)  # to avoid failing plan
+
+        yield from ["", ""]
+
+    def _generate_phase_transition(
+        self, phase: Phase | None = None, next_phase: Phase | None = None
+    ) -> Iterable[str]:
+        yield "+!transition_phase"
+
+        if phase is None and next_phase is None:  # base case true to avoid failing plan
+            yield f"{self.arrow_prefix}true."
+            return
+
+        yield f"{self.colon_prefix}phase({phase.id if phase else 'start'})"
+        yield f"{self.arrow_prefix}-+phase({next_phase.id if next_phase else 'end'})."
+
+    def _generate_norms(self, program: Program) -> Iterable[str]:
+        yield "// --- Norms ---"
+
+        for phase in program.phases:
+            for norm in phase.norms:
+                if type(norm) is BasicNorm:
+                    yield f"{self._slugify(norm)} :- phase({phase.id})."
+                if type(norm) is ConditionalNorm:
+                    yield (
+                        f"{self._slugify(norm)} :- phase({phase.id}) & "
+                        f"{self._slugify(norm.condition)}."
+                    )
+
+        yield from ["", ""]
+
+    def _generate_belief_inference(self, program: Program) -> Iterable[str]:
+        yield "// --- Belief inference rules ---"
+
+        for phase in program.phases:
+            for norm in phase.norms:
+                if not isinstance(norm, ConditionalNorm):
+                    continue
+
+                yield from self._belief_inference_recursive(norm.condition)
+
+            for trigger in phase.triggers:
+                yield from self._belief_inference_recursive(trigger.condition)
+
+        yield from ["", ""]
+
+    def _belief_inference_recursive(self, belief: Belief) -> Iterable[str]:
+        if type(belief) is KeywordBelief:
+            yield (
+                f"{self._slugify(belief)} :- user_said(Message) & "
+                f'.substring(Message, "{belief.keyword}", Pos) & Pos >= 0.'
+            )
+        if type(belief) is InferredBelief:
+            yield (
+                f"{self._slugify(belief)} :- {self._slugify(belief.left)} "
+                f"{'&' if belief.operator == LogicalOperator.AND else '|'} "
+                f"{self._slugify(belief.right)}."
+            )
+
+            yield from self._belief_inference_recursive(belief.left)
+            yield from self._belief_inference_recursive(belief.right)
+
+    def _generate_goals(self, program: Program) -> Iterable[str]:
+        yield "// --- Goals ---"
+
+        for phase in program.phases:
+            previous_goal: Goal | None = None
+            for goal in phase.goals:
+                yield from self._generate_goal_plan_recursive(goal, phase, previous_goal)
+                previous_goal = goal
+
+        yield from ["", ""]
+
+    def _generate_goal_plan_recursive(
+        self, goal: Goal, phase: Phase, previous_goal: Goal | None = None
+    ) -> Iterable[str]:
+        yield f"+{self._slugify(goal, include_prefix=True)}"
+
+        # Context
+        yield f"{self.colon_prefix}phase({phase.id}) &"
+        yield f"{self.indent_prefix}not responded_this_turn &"
+        yield f"{self.indent_prefix}not achieved_{self._slugify(goal)} &"
+        if previous_goal:
+            yield f"{self.indent_prefix}achieved_{self._slugify(previous_goal)}"
+        else:
+            yield f"{self.indent_prefix}true"
+
+        extra_goals_to_generate = []
+
+        steps = goal.plan.steps
+
+        if len(steps) == 0:
+            yield f"{self.arrow_prefix}true."
+            return
+
+        first_step = steps[0]
+        yield (
+            f"{self.arrow_prefix}{self._slugify(first_step, include_prefix=True)}"
+            f"{'.' if len(steps) == 1 and goal.can_fail else ';'}"
+        )
+        if isinstance(first_step, Goal):
+            extra_goals_to_generate.append(first_step)
+
+        for step in steps[1:-1]:
+            yield f"{self.indent_prefix}{self._slugify(step, include_prefix=True)};"
+            if isinstance(step, Goal):
+                extra_goals_to_generate.append(step)
+
+        if len(steps) > 1:
+            last_step = steps[-1]
+            yield (
+                f"{self.indent_prefix}{self._slugify(last_step, include_prefix=True)}"
+                f"{'.' if goal.can_fail else ';'}"
+            )
+            if isinstance(last_step, Goal):
+                extra_goals_to_generate.append(last_step)
+
+        if not goal.can_fail:
+            yield f"{self.indent_prefix}+achieved_{self._slugify(goal)}."
+
+        yield f"+{self._slugify(goal, include_prefix=True)}"
+        yield f"{self.arrow_prefix}true."
+
+        yield ""
+
+        extra_previous_goal: Goal | None = None
+        for extra_goal in extra_goals_to_generate:
+            yield from self._generate_goal_plan_recursive(extra_goal, phase, extra_previous_goal)
+            extra_previous_goal = extra_goal
+
+    def _generate_triggers(self, program: Program) -> Iterable[str]:
+        yield "// --- Triggers ---"
+
+        for phase in program.phases:
+            for trigger in phase.triggers:
+                yield from self._generate_trigger_plan(trigger, phase)
+
+        yield from ["", ""]
+
+    def _generate_trigger_plan(self, trigger: Trigger, phase: Phase) -> Iterable[str]:
+        belief_name = self._slugify(trigger.condition)
+
+        yield f"+{belief_name}"
+        yield f"{self.colon_prefix}phase({phase.id})"
+
+        extra_goals_to_generate = []
+
+        steps = trigger.plan.steps
+
+        if len(steps) == 0:
+            yield f"{self.arrow_prefix}true."
+            return
+
+        first_step = steps[0]
+        yield (
+            f"{self.arrow_prefix}{self._slugify(first_step, include_prefix=True)}"
+            f"{'.' if len(steps) == 1 else ';'}"
+        )
+        if isinstance(first_step, Goal):
+            extra_goals_to_generate.append(first_step)
+
+        for step in steps[1:-1]:
+            yield f"{self.indent_prefix}{self._slugify(step, include_prefix=True)};"
+            if isinstance(step, Goal):
+                extra_goals_to_generate.append(step)
+
+        if len(steps) > 1:
+            last_step = steps[-1]
+            yield f"{self.indent_prefix}{self._slugify(last_step, include_prefix=True)}."
+            if isinstance(last_step, Goal):
+                extra_goals_to_generate.append(last_step)
+
+        yield ""
+
+        extra_previous_goal: Goal | None = None
+        for extra_goal in extra_goals_to_generate:
+            yield from self._generate_trigger_plan_recursive(extra_goal, phase, extra_previous_goal)
+            extra_previous_goal = extra_goal
+
+    def _generate_trigger_plan_recursive(
+        self, goal: Goal, phase: Phase, previous_goal: Goal | None = None
+    ) -> Iterable[str]:
+        yield f"+{self._slugify(goal, include_prefix=True)}"
+
+        extra_goals_to_generate = []
+
+        steps = goal.plan.steps
+
+        if len(steps) == 0:
+            yield f"{self.arrow_prefix}true."
+            return
+
+        first_step = steps[0]
+        yield (
+            f"{self.arrow_prefix}{self._slugify(first_step, include_prefix=True)}"
+            f"{'.' if len(steps) == 1 and goal.can_fail else ';'}"
+        )
+        if isinstance(first_step, Goal):
+            extra_goals_to_generate.append(first_step)
+
+        for step in steps[1:-1]:
+            yield f"{self.indent_prefix}{self._slugify(step, include_prefix=True)};"
+            if isinstance(step, Goal):
+                extra_goals_to_generate.append(step)
+
+        if len(steps) > 1:
+            last_step = steps[-1]
+            yield (
+                f"{self.indent_prefix}{self._slugify(last_step, include_prefix=True)}"
+                f"{'.' if goal.can_fail else ';'}"
+            )
+            if isinstance(last_step, Goal):
+                extra_goals_to_generate.append(last_step)
+
+        if not goal.can_fail:
+            yield f"{self.indent_prefix}+achieved_{self._slugify(goal)}."
+
+        yield f"+{self._slugify(goal, include_prefix=True)}"
+        yield f"{self.arrow_prefix}true."
+
+        yield ""
+
+        extra_previous_goal: Goal | None = None
+        for extra_goal in extra_goals_to_generate:
+            yield from self._generate_goal_plan_recursive(extra_goal, phase, extra_previous_goal)
+            extra_previous_goal = extra_goal
+
+    def _slugify(self, element: ProgramElement, include_prefix: bool = False) -> str:
+        def base_slugify_call(text: str):
+            return slugify(text, separator="_", stopwords=["a", "the"])
+
+        if type(element) is KeywordBelief:
+            return f'keyword_said("{element.keyword}")'
+
+        if type(element) is SemanticBelief:
+            name = element.name
+            return f"semantic_{base_slugify_call(name if name else element.description)}"
+
+        if isinstance(element, BasicNorm):
+            return f'norm("{element.norm}")'
+
+        if isinstance(element, Goal):
+            return f"{'!' if include_prefix else ''}{base_slugify_call(element.name)}"
+
+        if isinstance(element, SpeechAction):
+            return f'.say("{element.text}")'
+
+        if isinstance(element, GestureAction):
+            return f'.gesture("{element.gesture}")'
+
+        if isinstance(element, LLMAction):
+            return f'!generate_response_with_goal("{element.goal}")'
+
+        if isinstance(element, Action.__value__):
+            raise NotImplementedError(
+                "Have not implemented an ASL string representation for this action."
+            )
+
+        if element.name == "":
+            raise ValueError("Name must be initialized for this type of ProgramElement.")
+
+        return base_slugify_call(element.name)
+
+    def _extract_basic_beliefs_from_program(self, program: Program) -> list[BasicBelief]:
+        beliefs = []
+
+        for phase in program.phases:
+            for norm in phase.norms:
+                if isinstance(norm, ConditionalNorm):
+                    beliefs += self._extract_basic_beliefs_from_belief(norm.condition)
+
+            for trigger in phase.triggers:
+                beliefs += self._extract_basic_beliefs_from_belief(trigger.condition)
+
+        return beliefs
+
+    def _extract_basic_beliefs_from_belief(self, belief: Belief) -> list[BasicBelief]:
+        if isinstance(belief, InferredBelief):
+            return self._extract_basic_beliefs_from_belief(
+                belief.left
+            ) + self._extract_basic_beliefs_from_belief(belief.right)
+        return [belief]
+
 
 class BDIProgramManager(BaseAgent):
     """
@@ -32,214 +607,44 @@ class BDIProgramManager(BaseAgent):
     :ivar sub_socket: The ZMQ SUB socket used to receive program updates.
     """
 
-    _program: Program
-    _phase: Phase | None
-
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.sub_socket = None
 
-    def _initialize_internal_state(self, program: Program):
+    # async def _send_to_bdi(self, program: Program):
-        self._program = program
+    #     """
-        self._phase = program.phases[0]  # start in first phase
+    #     Convert a received program into BDI beliefs and send them to the BDI Core Agent.
-        self._goal_mapping: dict[str, Goal] = {}
+    #
-        for phase in program.phases:
+    #     Currently, it takes the **first phase** of the program and extracts:
-            for goal in phase.goals:
+    #     -   **Norms**: Constraints or rules the agent must follow.
-                self._populate_goal_mapping_with_goal(goal)
+    #     -   **Goals**: Objectives the agent must achieve.
-
+    #
-    def _populate_goal_mapping_with_goal(self, goal: Goal):
+    #     These are sent as a ``BeliefMessage`` with ``replace=True``, meaning they will
-        self._goal_mapping[str(goal.id)] = goal
+    #     overwrite any existing norms/goals of the same name in the BDI agent.
-        for step in goal.plan.steps:
+    #
-            if isinstance(step, Goal):
+    #     :param program: The program object received from the API.
-                self._populate_goal_mapping_with_goal(step)
+    #     """
-
+    #     first_phase = program.phases[0]
-    async def _create_agentspeak_and_send_to_bdi(self, program: Program):
+    #     norms_belief = Belief(
-        """
+    #         name="norms",
-        Convert a received program into an AgentSpeak file and send it to the BDI Core Agent.
+    #         arguments=[norm.norm for norm in first_phase.norms],
-
+    #         replace=True,
-        :param program: The program object received from the API.
+    #     )
-        """
+    #     goals_belief = Belief(
-        asg = AgentSpeakGenerator()
+    #         name="goals",
-
+    #         arguments=[goal.description for goal in first_phase.goals],
-        asl_str = asg.generate(program)
+    #         replace=True,
-
+    #     )
-        file_name = "src/control_backend/agents/bdi/agentspeak.asl"
+    #     program_beliefs = BeliefMessage(beliefs=[norms_belief, goals_belief])
-
+    #
-        with open(file_name, "w") as f:
+    #     message = InternalMessage(
-            f.write(asl_str)
+    #         to=settings.agent_settings.bdi_core_name,
-
+    #         sender=self.name,
-        msg = InternalMessage(
+    #         body=program_beliefs.model_dump_json(),
-            sender=self.name,
+    #         thread="beliefs",
-            to=settings.agent_settings.bdi_core_name,
+    #     )
-            body=file_name,
+    #     await self.send(message)
-            thread="new_program",
+    #     self.logger.debug("Sent new norms and goals to the BDI agent.")
-        )
-
-        await self.send(msg)
-
-    async def handle_message(self, msg: InternalMessage):
-        match msg.thread:
-            case "transition_phase":
-                phases = json.loads(msg.body)
-
-                await self._transition_phase(phases["old"], phases["new"])
-            case "achieve_goal":
-                goal_id = msg.body
-                await self._send_achieved_goal_to_semantic_belief_extractor(goal_id)
-
-    async def _transition_phase(self, old: str, new: str):
-        if old != str(self._phase.id):
-            self.logger.warning(
-                f"Phase transition desync detected! ASL requested move from '{old}', "
-                f"but Python is currently in '{self._phase.id}'. Request ignored."
-            )
-            return
-
-        if new == "end":
-            self._phase = None
-            # Notify user interaction agent
-            msg = InternalMessage(
-                to=settings.agent_settings.user_interrupt_name,
-                thread="transition_phase",
-                body="end",
-            )
-            self.logger.info("Transitioned to end phase, notifying UserInterruptAgent.")
-
-            self.add_behavior(self.send(msg))
-            return
-
-        for phase in self._program.phases:
-            if str(phase.id) == new:
-                self._phase = phase
-
-        await self._send_beliefs_to_semantic_belief_extractor()
-        await self._send_goals_to_semantic_belief_extractor()
-
-        # Notify user interaction agent
-        msg = InternalMessage(
-            to=settings.agent_settings.user_interrupt_name,
-            thread="transition_phase",
-            body=str(self._phase.id),
-        )
-        self.logger.info(f"Transitioned to phase {new}, notifying UserInterruptAgent.")
-
-        self.add_behavior(self.send(msg))
-
-    def _extract_current_beliefs(self) -> list[Belief]:
-        beliefs: list[Belief] = []
-
-        for norm in self._phase.norms:
-            if isinstance(norm, ConditionalNorm):
-                beliefs += self._extract_beliefs_from_belief(norm.condition)
-
-        for trigger in self._phase.triggers:
-            beliefs += self._extract_beliefs_from_belief(trigger.condition)
-
-        return beliefs
-
-    @staticmethod
-    def _extract_beliefs_from_belief(belief: Belief) -> list[Belief]:
-        if isinstance(belief, InferredBelief):
-            return BDIProgramManager._extract_beliefs_from_belief(
-                belief.left
-            ) + BDIProgramManager._extract_beliefs_from_belief(belief.right)
-        return [belief]
-
-    async def _send_beliefs_to_semantic_belief_extractor(self):
-        """
-        Extract beliefs from the program and send them to the Semantic Belief Extractor Agent.
-        """
-        beliefs = BeliefList(beliefs=self._extract_current_beliefs())
-
-        message = InternalMessage(
-            to=settings.agent_settings.text_belief_extractor_name,
-            sender=self.name,
-            body=beliefs.model_dump_json(),
-            thread="beliefs",
-        )
-
-        await self.send(message)
-
-    @staticmethod
-    def _extract_goals_from_goal(goal: Goal) -> list[Goal]:
-        """
-        Extract all goals from a given goal, that is: the goal itself and any subgoals.
-
-        :return: All goals within and including the given goal.
-        """
-        goals: list[Goal] = [goal]
-        for plan in goal.plan:
-            if isinstance(plan, Goal):
-                goals.extend(BDIProgramManager._extract_goals_from_goal(plan))
-        return goals
-
-    def _extract_current_goals(self) -> list[Goal]:
-        """
-        Extract all goals from the program, including subgoals.
-
-        :return: A list of Goal objects.
-        """
-        goals: list[Goal] = []
-
-        for goal in self._phase.goals:
-            goals.extend(self._extract_goals_from_goal(goal))
-
-        return goals
-
-    async def _send_goals_to_semantic_belief_extractor(self):
-        """
-        Extract goals for the current phase and send them to the Semantic Belief Extractor Agent.
-        """
-        goals = GoalList(goals=self._extract_current_goals())
-
-        message = InternalMessage(
-            to=settings.agent_settings.text_belief_extractor_name,
-            sender=self.name,
-            body=goals.model_dump_json(),
-            thread="goals",
-        )
-
-        await self.send(message)
-
-    async def _send_achieved_goal_to_semantic_belief_extractor(self, achieved_goal_id: str):
-        """
-        Inform the semantic belief extractor when a goal is marked achieved.
-
-        :param achieved_goal_id: The id of the achieved goal.
-        """
-        goal = self._goal_mapping.get(achieved_goal_id)
-        if goal is None:
-            self.logger.debug(f"Goal with ID {achieved_goal_id} marked achieved but was not found.")
-            return
-
-        goals = self._extract_goals_from_goal(goal)
-        message = InternalMessage(
-            to=settings.agent_settings.text_belief_extractor_name,
-            body=GoalList(goals=goals).model_dump_json(),
-            thread="achieved_goals",
-        )
-        await self.send(message)
-
-    async def _send_clear_llm_history(self):
-        """
-        Clear the LLM Agent's conversation history.
-
-        Sends an empty history to the LLM Agent to reset its state.
-        """
-        message = InternalMessage(
-            to=settings.agent_settings.llm_name,
-            body="clear_history",
-        )
-        await self.send(message)
-        self.logger.debug("Sent message to LLM agent to clear history.")
-
-        extractor_msg = InternalMessage(
-            to=settings.agent_settings.text_belief_extractor_name,
-            thread="conversation_history",
-            body="reset",
-        )
-        await self.send(extractor_msg)
-        self.logger.debug("Sent message to extractor agent to clear history.")
 
     async def _receive_programs(self):
         """
@@ -247,7 +652,6 @@ class BDIProgramManager(BaseAgent):
 
         It listens to the ``program`` topic on the internal ZMQ SUB socket.
         When a program is received, it is validated and forwarded to BDI via :meth:`_send_to_bdi`.
-        Additionally, the LLM history is cleared via :meth:`_send_clear_llm_history`.
         """
         while True:
             topic, body = await self.sub_socket.recv_multipart()
@@ -255,43 +659,18 @@ class BDIProgramManager(BaseAgent):
             try:
                 program = Program.model_validate_json(body)
             except ValidationError:
-                self.logger.warning("Received an invalid program.")
+                self.logger.exception("Received an invalid program.")
                 continue
 
-            self._initialize_internal_state(program)
+            await self._send_to_bdi(program)
-            await self._send_program_to_user_interrupt(program)
-            await self._send_clear_llm_history()
-
-            await asyncio.gather(
-                self._create_agentspeak_and_send_to_bdi(program),
-                self._send_beliefs_to_semantic_belief_extractor(),
-                self._send_goals_to_semantic_belief_extractor(),
-            )
-
-    async def _send_program_to_user_interrupt(self, program: Program):
-        """
-        Send the received program to the User Interrupt Agent.
-
-        :param program: The program object received from the API.
-        """
-        msg = InternalMessage(
-            sender=self.name,
-            to=settings.agent_settings.user_interrupt_name,
-            body=program.model_dump_json(),
-            thread="new_program",
-        )
-
-        await self.send(msg)
 
     async def setup(self):
         """
         Initialize the agent.
 
         Connects the internal ZMQ SUB socket and subscribes to the 'program' topic.
-        Starts the background behavior to receive programs. Initializes a default program.
+        Starts the background behavior to receive programs.
         """
-        await self._create_agentspeak_and_send_to_bdi(Program(phases=[]))
-
         context = Context.instance()
 
         self.sub_socket = context.socket(zmq.SUB)
@@ -299,3 +678,7 @@ class BDIProgramManager(BaseAgent):
         self.sub_socket.subscribe("program")
 
         self.add_behavior(self._receive_programs())
+
+
+if __name__ == "__main__":
+    do_things()

src/control_backend/agents/bdi/default_behavior.asl

@@ -1,34 +0,0 @@
-phase("end").
-keyword_said(Keyword) :- (user_said(Message) & .substring(Keyword, Message, Pos)) & (Pos >= 0).
-
-
-+!reply_with_goal(Goal)
-  :   user_said(Message)
-  <-  +responded_this_turn;
-      .findall(Norm, norm(Norm), Norms);
-      .reply_with_goal(Message, Norms, Goal).
-
-+!say(Text)
-  <-  +responded_this_turn;
-      .say(Text).
-
-+!reply
-  :   user_said(Message)
-  <-  +responded_this_turn;
-      .findall(Norm, norm(Norm), Norms);
-      .reply(Message, Norms).
-
-+!notify_cycle
-  <-  .notify_ui;
-      .wait(1).
-
-+user_said(Message)
-  :   phase("end")
-  <-  .notify_user_said(Message);
-      !reply.
-
-+!check_triggers
-  <-  true.
-
-+!transition_phase
-  <-  true.

src/control_backend/agents/bdi/rules.asl

@@ -0,0 +1,6 @@
+norms("").
+goals("").
+
++user_said(Message) : norms(Norms) & goals(Goals) <-
+  -user_said(Message);
+  .reply(Message, Norms, Goals).

src/control_backend/agents/bdi/text_belief_extractor_agent.py

@@ -2,46 +2,23 @@ import asyncio
 import json
 
 import httpx
-from pydantic import BaseModel, ValidationError
+from pydantic import ValidationError
+from slugify import slugify
 
 from control_backend.agents.base import BaseAgent
-from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
-from control_backend.schemas.belief_list import BeliefList, GoalList
 from control_backend.schemas.belief_message import Belief as InternalBelief
 from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.chat_history import ChatHistory, ChatMessage
-from control_backend.schemas.program import BaseGoal, SemanticBelief
+from control_backend.schemas.program import (
-
+    Belief,
-type JSONLike = None | bool | int | float | str | list["JSONLike"] | dict[str, "JSONLike"]
+    ConditionalNorm,
-
+    InferredBelief,
-
+    Program,
-class BeliefState(BaseModel):
+    SemanticBelief,
-    true: set[InternalBelief] = set()
-    false: set[InternalBelief] = set()
-
-    def difference(self, other: "BeliefState") -> "BeliefState":
-        return BeliefState(
-            true=self.true - other.true,
-            false=self.false - other.false,
 )
 
-    def union(self, other: "BeliefState") -> "BeliefState":
-        return BeliefState(
-            true=self.true | other.true,
-            false=self.false | other.false,
-        )
-
-    def __sub__(self, other):
-        return self.difference(other)
-
-    def __or__(self, other):
-        return self.union(other)
-
-    def __bool__(self):
-        return bool(self.true) or bool(self.false)
-
 
 class TextBeliefExtractorAgent(BaseAgent):
     """
@@ -57,12 +34,8 @@ class TextBeliefExtractorAgent(BaseAgent):
 
     def __init__(self, name: str):
         super().__init__(name)
-        self._llm = self.LLM(self, settings.llm_settings.n_parallel)
+        self.beliefs: dict[str, bool] = {}
-        self.belief_inferrer = SemanticBeliefInferrer(self._llm)
+        self.available_beliefs: list[SemanticBelief] = []
-        self.goal_inferrer = GoalAchievementInferrer(self._llm)
-        self._current_beliefs = BeliefState()
-        self._current_goal_completions: dict[str, bool] = {}
-        self._force_completed_goals: set[BaseGoal] = set()
         self.conversation = ChatHistory(messages=[])
 
     async def setup(self):
@@ -84,14 +57,13 @@ class TextBeliefExtractorAgent(BaseAgent):
             case settings.agent_settings.transcription_name:
                 self.logger.debug("Received text from transcriber: %s", msg.body)
                 self._apply_conversation_message(ChatMessage(role="user", content=msg.body))
-                await self._user_said(msg.body)
                 await self._infer_new_beliefs()
-                await self._infer_goal_completions()
+                await self._user_said(msg.body)
             case settings.agent_settings.llm_name:
                 self.logger.debug("Received text from LLM: %s", msg.body)
                 self._apply_conversation_message(ChatMessage(role="assistant", content=msg.body))
             case settings.agent_settings.bdi_program_manager_name:
-                await self._handle_program_manager_message(msg)
+                self._handle_program_manager_message(msg)
             case _:
                 self.logger.info("Discarding message from %s", sender)
                 return
@@ -106,86 +78,51 @@ class TextBeliefExtractorAgent(BaseAgent):
         length_limit = settings.behaviour_settings.conversation_history_length_limit
         self.conversation.messages = (self.conversation.messages + [message])[-length_limit:]
 
-    async def _handle_program_manager_message(self, msg: InternalMessage):
+    def _handle_program_manager_message(self, msg: InternalMessage):
         """
-        Handle a message from the program manager: extract available beliefs and goals from it.
+        Handle a message from the program manager: extract available beliefs from it.
 
         :param msg: The received message from the program manager.
         """
-        match msg.thread:
-            case "beliefs":
-                self._handle_beliefs_message(msg)
-                await self._infer_new_beliefs()
-            case "goals":
-                self._handle_goals_message(msg)
-                await self._infer_goal_completions()
-            case "achieved_goals":
-                self._handle_goal_achieved_message(msg)
-            case "conversation_history":
-                if msg.body == "reset":
-                    self._reset_phase()
-            case _:
-                self.logger.warning("Received unexpected message from %s", msg.sender)
-
-    def _reset_phase(self):
-        self.conversation = ChatHistory(messages=[])
-        self.belief_inferrer.available_beliefs.clear()
-        self._current_beliefs = BeliefState()
-        self.goal_inferrer.goals.clear()
-        self._current_goal_completions = {}
-
-    def _handle_beliefs_message(self, msg: InternalMessage):
         try:
-            belief_list = BeliefList.model_validate_json(msg.body)
+            program = Program.model_validate_json(msg.body)
         except ValidationError:
             self.logger.warning(
-                "Received message from program manager but it is not a valid list of beliefs."
+                "Received message from program manager but it is not a valid program."
             )
             return
 
-        available_beliefs = [b for b in belief_list.beliefs if isinstance(b, SemanticBelief)]
+        self.logger.debug("Received a program from the program manager.")
-        self.belief_inferrer.available_beliefs = available_beliefs
+
-        self.logger.debug(
+        self.available_beliefs = self._extract_basic_beliefs_from_program(program)
-            "Received %d semantic beliefs from the program manager: %s",
+
-            len(available_beliefs),
+    # TODO Copied from an incomplete version of the program manager. Use that one instead.
-            ", ".join(b.name for b in available_beliefs),
+    @staticmethod
+    def _extract_basic_beliefs_from_program(program: Program) -> list[SemanticBelief]:
+        beliefs = []
+
+        for phase in program.phases:
+            for norm in phase.norms:
+                if isinstance(norm, ConditionalNorm):
+                    beliefs += TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(
+                        norm.condition
                     )
 
-    def _handle_goals_message(self, msg: InternalMessage):
+            for trigger in phase.triggers:
-        try:
+                beliefs += TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(
-            goals_list = GoalList.model_validate_json(msg.body)
+                    trigger.condition
-        except ValidationError:
-            self.logger.warning(
-                "Received message from program manager but it is not a valid list of goals."
-            )
-            return
-
-        # Use only goals that can fail, as the others are always assumed to be completed
-        available_goals = {g for g in goals_list.goals if g.can_fail}
-        available_goals -= self._force_completed_goals
-        self.goal_inferrer.goals = available_goals
-        self.logger.debug(
-            "Received %d failable goals from the program manager: %s",
-            len(available_goals),
-            ", ".join(g.name for g in available_goals),
                 )
 
-    def _handle_goal_achieved_message(self, msg: InternalMessage):
+        return beliefs
-        # NOTE: When goals can be marked unachieved, remember to re-add them to the goal_inferrer
-        try:
-            goals_list = GoalList.model_validate_json(msg.body)
-        except ValidationError:
-            self.logger.warning(
-                "Received goal achieved message from the program manager, "
-                "but it is not a valid list of goals."
-            )
-            return
 
-        for goal in goals_list.goals:
+    # TODO Copied from an incomplete version of the program manager. Use that one instead.
-            self._force_completed_goals.add(goal)
+    @staticmethod
-            self._current_goal_completions[f"achieved_{AgentSpeakGenerator.slugify(goal)}"] = True
+    def _extract_basic_beliefs_from_belief(belief: Belief) -> list[SemanticBelief]:
-
+        if isinstance(belief, InferredBelief):
-        self.goal_inferrer.goals -= self._force_completed_goals
+            return TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(
+                belief.left
+            ) + TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(belief.right)
+        return [belief]
 
     async def _user_said(self, text: str):
         """
@@ -193,88 +130,161 @@ class TextBeliefExtractorAgent(BaseAgent):
 
         :param text: User's transcribed text.
         """
+        belief = {"beliefs": {"user_said": [text]}, "type": "belief_extraction_text"}
+        payload = json.dumps(belief)
+
         belief_msg = InternalMessage(
-            to=settings.agent_settings.bdi_core_name,
+            to=settings.agent_settings.bdi_belief_collector_name,
             sender=self.name,
-            body=BeliefMessage(
+            body=payload,
-                replace=[InternalBelief(name="user_said", arguments=[text])],
-            ).model_dump_json(),
             thread="beliefs",
         )
         await self.send(belief_msg)
 
     async def _infer_new_beliefs(self):
-        conversation_beliefs = await self.belief_inferrer.infer_from_conversation(self.conversation)
+        """
-
+        Process conversation history to extract beliefs, semantically. Any changed beliefs are sent
-        new_beliefs = conversation_beliefs - self._current_beliefs
+        to the BDI core.
-        if not new_beliefs:
+        """
-            self.logger.debug("No new beliefs detected.")
+        # Return instantly if there are no beliefs to infer
+        if not self.available_beliefs:
             return
 
-        self._current_beliefs |= new_beliefs
+        candidate_beliefs = await self._infer_turn()
+        belief_changes = BeliefMessage()
+        for belief_key, belief_value in candidate_beliefs.items():
+            if belief_value is None:
+                continue
+            old_belief_value = self.beliefs.get(belief_key)
+            if belief_value == old_belief_value:
+                continue
 
-        belief_changes = BeliefMessage(
+            self.beliefs[belief_key] = belief_value
-            create=list(new_beliefs.true),
-            delete=list(new_beliefs.false),
-        )
 
-        message = InternalMessage(
+            belief = InternalBelief(name=belief_key, arguments=None)
+            if belief_value:
+                belief_changes.create.append(belief)
+            else:
+                belief_changes.delete.append(belief)
+
+        # Return if there were no changes in beliefs
+        if not belief_changes.has_values():
+            return
+
+        beliefs_message = InternalMessage(
             to=settings.agent_settings.bdi_core_name,
             sender=self.name,
             body=belief_changes.model_dump_json(),
             thread="beliefs",
         )
-        await self.send(message)
+        await self.send(beliefs_message)
 
-    async def _infer_goal_completions(self):
+    @staticmethod
-        goal_completions = await self.goal_inferrer.infer_from_conversation(self.conversation)
+    def _split_into_chunks[T](items: list[T], n: int) -> list[list[T]]:
+        k, m = divmod(len(items), n)
+        return [items[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)]
 
-        new_achieved = [
+    async def _infer_turn(self) -> dict:
-            InternalBelief(name=goal, arguments=None)
-            for goal, achieved in goal_completions.items()
-            if achieved and self._current_goal_completions.get(goal) != achieved
-        ]
-        new_not_achieved = [
-            InternalBelief(name=goal, arguments=None)
-            for goal, achieved in goal_completions.items()
-            if not achieved and self._current_goal_completions.get(goal) != achieved
-        ]
-        for goal, achieved in goal_completions.items():
-            self._current_goal_completions[goal] = achieved
-
-        if not new_achieved and not new_not_achieved:
-            self.logger.debug("No goal achievement changes detected.")
-            return
-
-        belief_changes = BeliefMessage(
-            create=new_achieved,
-            delete=new_not_achieved,
-        )
-        message = InternalMessage(
-            to=settings.agent_settings.bdi_core_name,
-            sender=self.name,
-            body=belief_changes.model_dump_json(),
-            thread="beliefs",
-        )
-        await self.send(message)
-
-    class LLM:
         """
-        Class that handles sending structured generation requests to an LLM.
+        Process the stored conversation history to extract semantic beliefs. Returns a list of
+        beliefs that have been set to ``True``, ``False`` or ``None``.
+
+        :return: A dict mapping belief names to a value ``True``, ``False`` or ``None``.
+        """
+        n_parallel = max(1, min(settings.llm_settings.n_parallel - 1, len(self.available_beliefs)))
+        all_beliefs = await asyncio.gather(
+            *[
+                self._infer_beliefs(self.conversation, beliefs)
+                for beliefs in self._split_into_chunks(self.available_beliefs, n_parallel)
+            ]
+        )
+        retval = {}
+        for beliefs in all_beliefs:
+            if beliefs is None:
+                continue
+            retval.update(beliefs)
+        return retval
+
+    @staticmethod
+    def _create_belief_schema(belief: SemanticBelief) -> tuple[str, dict]:
+        # TODO: use real belief names
+        return belief.name or slugify(belief.description), {
+            "type": ["boolean", "null"],
+            "description": belief.description,
+        }
+
+    @staticmethod
+    def _create_beliefs_schema(beliefs: list[SemanticBelief]) -> dict:
+        belief_schemas = [
+            TextBeliefExtractorAgent._create_belief_schema(belief) for belief in beliefs
+        ]
+
+        return {
+            "type": "object",
+            "properties": dict(belief_schemas),
+            "required": [name for name, _ in belief_schemas],
+        }
+
+    @staticmethod
+    def _format_message(message: ChatMessage):
+        return f"{message.role.upper()}:\n{message.content}"
+
+    @staticmethod
+    def _format_conversation(conversation: ChatHistory):
+        return "\n\n".join(
+            [TextBeliefExtractorAgent._format_message(message) for message in conversation.messages]
+        )
+
+    @staticmethod
+    def _format_beliefs(beliefs: list[SemanticBelief]):
+        # TODO: use real belief names
+        return "\n".join(
+            [
+                f"- {belief.name or slugify(belief.description)}: {belief.description}"
+                for belief in beliefs
+            ]
+        )
+
+    async def _infer_beliefs(
+        self,
+        conversation: ChatHistory,
+        beliefs: list[SemanticBelief],
+    ) -> dict | None:
+        """
+        Infer given beliefs based on the given conversation.
+        :param conversation: The conversation to infer beliefs from.
+        :param beliefs: The beliefs to infer.
+        :return: A dict containing belief names and a boolean whether they hold, or None if the
+            belief cannot be inferred based on the given conversation.
+        """
+        example = {
+            "example_belief": True,
+        }
+
+        prompt = f"""{self._format_conversation(conversation)}
+
+Given the above conversation, what beliefs can be inferred?
+If there is no relevant information about a belief belief, give null.
+In case messages conflict, prefer using the most recent messages for inference.
+
+Choose from the following list of beliefs, formatted as (belief_name, description):
+{self._format_beliefs(beliefs)}
+
+Respond with a JSON similar to the following, but with the property names as given above:
+{json.dumps(example, indent=2)}
 """
 
-        def __init__(self, agent: "TextBeliefExtractorAgent", n_parallel: int):
+        schema = self._create_beliefs_schema(beliefs)
-            self._agent = agent
-            self._semaphore = asyncio.Semaphore(n_parallel)
 
-        async def query(self, prompt: str, schema: dict, tries: int = 3) -> JSONLike | None:
+        return await self._retry_query_llm(prompt, schema)
+
+    async def _retry_query_llm(self, prompt: str, schema: dict, tries: int = 3) -> dict | None:
         """
         Query the LLM with the given prompt and schema, return an instance of a dict conforming
         to this schema. Try ``tries`` times, or return None.
 
         :param prompt: Prompt to be queried.
         :param schema: Schema to be queried.
-            :param tries: Number of times to try to query the LLM.
         :return: An instance of a dict conforming to this schema, or None if failed.
         """
         try_count = 0
@@ -286,7 +296,7 @@ class TextBeliefExtractorAgent(BaseAgent):
             except (httpx.HTTPError, json.JSONDecodeError, KeyError) as e:
                 if try_count < tries:
                     continue
-                    self._agent.logger.exception(
+                self.logger.exception(
                     "Failed to get LLM response after %d tries.",
                     try_count,
                     exc_info=e,
@@ -294,10 +304,11 @@ class TextBeliefExtractorAgent(BaseAgent):
 
         return None
 
-        async def _query_llm(self, prompt: str, schema: dict) -> JSONLike:
+    @staticmethod
+    async def _query_llm(prompt: str, schema: dict) -> dict:
         """
-            Query an LLM with the given prompt and schema, return an instance of a dict conforming
+        Query an LLM with the given prompt and schema, return an instance of a dict conforming to
-            to that schema.
+        that schema.
 
         :param prompt: The prompt to be queried.
         :param schema: Schema to use during response.
@@ -305,10 +316,8 @@ class TextBeliefExtractorAgent(BaseAgent):
         :raises httpx.HTTPStatusError: If the LLM server responded with an error.
         :raises json.JSONDecodeError: If the LLM response was not valid JSON. May happen if the
             response was cut off early due to length limitations.
-            :raises KeyError: If the LLM server responded with no error, but the response was
+        :raises KeyError: If the LLM server responded with no error, but the response was invalid.
-                invalid.
         """
-            async with self._semaphore:
         async with httpx.AsyncClient() as client:
             response = await client.post(
                 settings.llm_settings.local_llm_url,
@@ -327,177 +336,10 @@ class TextBeliefExtractorAgent(BaseAgent):
                     "temperature": settings.llm_settings.code_temperature,
                     "stream": False,
                 },
-                        timeout=30.0,
+                timeout=None,
             )
             response.raise_for_status()
 
             response_json = response.json()
             json_message = response_json["choices"][0]["message"]["content"]
             return json.loads(json_message)
-
-
-class SemanticBeliefInferrer:
-    """
-    Class that handles only prompting an LLM for semantic beliefs.
-    """
-
-    def __init__(
-        self,
-        llm: "TextBeliefExtractorAgent.LLM",
-        available_beliefs: list[SemanticBelief] | None = None,
-    ):
-        self._llm = llm
-        self.available_beliefs: list[SemanticBelief] = available_beliefs or []
-
-    async def infer_from_conversation(self, conversation: ChatHistory) -> BeliefState:
-        """
-        Process conversation history to extract beliefs, semantically. The result is an object that
-        describes all beliefs that hold or don't hold based on the full conversation.
-
-        :param conversation: The conversation history to be processed.
-        :return: An object that describes beliefs.
-        """
-        # Return instantly if there are no beliefs to infer
-        if not self.available_beliefs:
-            return BeliefState()
-
-        n_parallel = max(1, min(settings.llm_settings.n_parallel - 1, len(self.available_beliefs)))
-        all_beliefs: list[dict[str, bool | None] | None] = await asyncio.gather(
-            *[
-                self._infer_beliefs(conversation, beliefs)
-                for beliefs in self._split_into_chunks(self.available_beliefs, n_parallel)
-            ]
-        )
-        retval = BeliefState()
-        for beliefs in all_beliefs:
-            if beliefs is None:
-                continue
-            for belief_name, belief_holds in beliefs.items():
-                if belief_holds is None:
-                    continue
-                belief = InternalBelief(name=belief_name, arguments=None)
-                if belief_holds:
-                    retval.true.add(belief)
-                else:
-                    retval.false.add(belief)
-        return retval
-
-    @staticmethod
-    def _split_into_chunks[T](items: list[T], n: int) -> list[list[T]]:
-        """
-        Split a list into ``n`` chunks, making each chunk approximately ``len(items) / n`` long.
-
-        :param items: The list of items to split.
-        :param n: The number of desired chunks.
-        :return: A list of chunks each approximately ``len(items) / n`` long.
-        """
-        k, m = divmod(len(items), n)
-        return [items[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)]
-
-    async def _infer_beliefs(
-        self,
-        conversation: ChatHistory,
-        beliefs: list[SemanticBelief],
-    ) -> dict[str, bool | None] | None:
-        """
-        Infer given beliefs based on the given conversation.
-        :param conversation: The conversation to infer beliefs from.
-        :param beliefs: The beliefs to infer.
-        :return: A dict containing belief names and a boolean whether they hold, or None if the
-            belief cannot be inferred based on the given conversation.
-        """
-        example = {
-            "example_belief": True,
-        }
-
-        prompt = f"""{self._format_conversation(conversation)}
-
-Given the above conversation, what beliefs can be inferred?
-If there is no relevant information about a belief belief, give null.
-In case messages conflict, prefer using the most recent messages for inference.
-
-Choose from the following list of beliefs, formatted as `- <belief_name>: <description>`:
-{self._format_beliefs(beliefs)}
-
-Respond with a JSON similar to the following, but with the property names as given above:
-{json.dumps(example, indent=2)}
-"""
-
-        schema = self._create_beliefs_schema(beliefs)
-
-        return await self._llm.query(prompt, schema)
-
-    @staticmethod
-    def _create_belief_schema(belief: SemanticBelief) -> tuple[str, dict]:
-        return AgentSpeakGenerator.slugify(belief), {
-            "type": ["boolean", "null"],
-            "description": belief.description,
-        }
-
-    @staticmethod
-    def _create_beliefs_schema(beliefs: list[SemanticBelief]) -> dict:
-        belief_schemas = [
-            SemanticBeliefInferrer._create_belief_schema(belief) for belief in beliefs
-        ]
-
-        return {
-            "type": "object",
-            "properties": dict(belief_schemas),
-            "required": [name for name, _ in belief_schemas],
-        }
-
-    @staticmethod
-    def _format_message(message: ChatMessage):
-        return f"{message.role.upper()}:\n{message.content}"
-
-    @staticmethod
-    def _format_conversation(conversation: ChatHistory):
-        return "\n\n".join(
-            [SemanticBeliefInferrer._format_message(message) for message in conversation.messages]
-        )
-
-    @staticmethod
-    def _format_beliefs(beliefs: list[SemanticBelief]):
-        return "\n".join(
-            [f"- {AgentSpeakGenerator.slugify(belief)}: {belief.description}" for belief in beliefs]
-        )
-
-
-class GoalAchievementInferrer(SemanticBeliefInferrer):
-    def __init__(self, llm: TextBeliefExtractorAgent.LLM):
-        super().__init__(llm)
-        self.goals: set[BaseGoal] = set()
-
-    async def infer_from_conversation(self, conversation: ChatHistory) -> dict[str, bool]:
-        """
-        Determine which goals have been achieved based on the given conversation.
-
-        :param conversation: The conversation to infer goal completion from.
-        :return: A mapping of goals and a boolean whether they have been achieved.
-        """
-        if not self.goals:
-            return {}
-
-        goals_achieved = await asyncio.gather(
-            *[self._infer_goal(conversation, g) for g in self.goals]
-        )
-        return {
-            f"achieved_{AgentSpeakGenerator.slugify(goal)}": achieved
-            for goal, achieved in zip(self.goals, goals_achieved, strict=True)
-        }
-
-    async def _infer_goal(self, conversation: ChatHistory, goal: BaseGoal) -> bool:
-        prompt = f"""{self._format_conversation(conversation)}
-
-Given the above conversation, what has the following goal been achieved?
-
-The name of the goal: {goal.name}
-Description of the goal: {goal.description}
-
-Answer with literally only `true` or `false` (without backticks)."""
-
-        schema = {
-            "type": "boolean",
-        }
-
-        return await self._llm.query(prompt, schema)

src/control_backend/agents/communication/ri_communication_agent.py

@@ -8,8 +8,8 @@ from zmq.asyncio import Context
 
 from control_backend.agents import BaseAgent
 from control_backend.agents.actuation.robot_gesture_agent import RobotGestureAgent
+from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
-from control_backend.schemas.internal_message import InternalMessage
 from control_backend.schemas.ri_message import PauseCommand
 
 from ..actuation.robot_speech_agent import RobotSpeechAgent
@@ -41,7 +41,7 @@ class RICommunicationAgent(BaseAgent):
     def __init__(
         self,
         name: str,
-        address=settings.zmq_settings.ri_communication_address,
+        address=settings.zmq_settings.ri_command_address,
         bind=False,
     ):
         super().__init__(name)
@@ -50,8 +50,6 @@ class RICommunicationAgent(BaseAgent):
         self._req_socket: azmq.Socket | None = None
         self.pub_socket: azmq.Socket | None = None
         self.connected = False
-        self.gesture_agent: RobotGestureAgent | None = None
-        self.speech_agent: RobotSpeechAgent | None = None
 
     async def setup(self):
         """
@@ -145,7 +143,6 @@ class RICommunicationAgent(BaseAgent):
 
             # At this point, we have a valid response
             try:
-                self.logger.debug("Negotiation successful. Handling rn")
                 await self._handle_negotiation_response(received_message)
                 # Let UI know that we're connected
                 topic = b"ping"
@@ -174,7 +171,7 @@ class RICommunicationAgent(BaseAgent):
             bind = port_data["bind"]
 
             if not bind:
-                addr = f"tcp://{settings.ri_host}:{port}"
+                addr = f"tcp://localhost:{port}"
             else:
                 addr = f"tcp://*:{port}"
 
@@ -194,7 +191,6 @@ class RICommunicationAgent(BaseAgent):
                         address=addr,
                         bind=bind,
                     )
-                    self.speech_agent = robot_speech_agent
                     robot_gesture_agent = RobotGestureAgent(
                         settings.agent_settings.robot_gesture_name,
                         address=addr,
@@ -202,7 +198,6 @@ class RICommunicationAgent(BaseAgent):
                         gesture_data=gesture_data,
                         single_gesture_data=single_gesture_data,
                     )
-                    self.gesture_agent = robot_gesture_agent
                     await robot_speech_agent.start()
                     await asyncio.sleep(0.1)  # Small delay
                     await robot_gesture_agent.start()
@@ -233,7 +228,6 @@ class RICommunicationAgent(BaseAgent):
         while self._running:
             if not self.connected:
                 await asyncio.sleep(settings.behaviour_settings.sleep_s)
-                self.logger.debug("Not connected, skipping ping loop iteration.")
                 continue
 
             # We need to listen and send pings.
@@ -257,7 +251,6 @@ class RICommunicationAgent(BaseAgent):
                     self._req_socket.recv_json(), timeout=seconds_to_wait_total / 2
                 )
 
-                if "endpoint" in message and message["endpoint"] != "ping":
                 self.logger.debug(f'Received message "{message}" from RI.')
                 if "endpoint" not in message:
                     self.logger.warning("No received endpoint in message, expected ping endpoint.")
@@ -298,27 +291,15 @@ class RICommunicationAgent(BaseAgent):
         # Tell UI we're disconnected.
         topic = b"ping"
         data = json.dumps(False).encode()
-        self.logger.debug("1")
         if self.pub_socket:
             try:
-                self.logger.debug("2")
                 await asyncio.wait_for(self.pub_socket.send_multipart([topic, data]), 5)
             except TimeoutError:
-                self.logger.debug("3")
                 self.logger.warning("Connection ping for router timed out.")
 
         # Try to reboot/renegotiate
-        if self.gesture_agent is not None:
-            await self.gesture_agent.stop()
-
-        if self.speech_agent is not None:
-            await self.speech_agent.stop()
-
-        if self.pub_socket is not None:
-            self.pub_socket.close()
-
         self.logger.debug("Restarting communication negotiation.")
-        if await self._negotiate_connection(max_retries=2):
+        if await self._negotiate_connection(max_retries=1):
             self.connected = True
 
     async def handle_message(self, msg : InternalMessage):

src/control_backend/agents/llm/llm_agent.py

@@ -46,23 +46,14 @@ class LLMAgent(BaseAgent):
         :param msg: The received internal message.
         """
         if msg.sender == settings.agent_settings.bdi_core_name:
-            match msg.thread:
+            self.logger.debug("Processing message from BDI core.")
-                case "prompt_message":
             try:
                 prompt_message = LLMPromptMessage.model_validate_json(msg.body)
                 await self._process_bdi_message(prompt_message)
             except ValidationError:
                 self.logger.debug("Prompt message from BDI core is invalid.")
-                case "assistant_message":
-                    self.history.append({"role": "assistant", "content": msg.body})
-                case "user_message":
-                    self.history.append({"role": "user", "content": msg.body})
-        elif msg.sender == settings.agent_settings.bdi_program_manager_name:
-            if msg.body == "clear_history":
-                self.logger.debug("Clearing conversation history.")
-                self.history.clear()
         else:
-            self.logger.debug("Message ignored.")
+            self.logger.debug("Message ignored (not from BDI core.")
 
     async def _process_bdi_message(self, message: LLMPromptMessage):
         """
@@ -123,6 +114,13 @@ class LLMAgent(BaseAgent):
         :param goals: Goals the LLM should achieve.
         :yield: Fragments of the LLM-generated content (e.g., sentences/phrases).
         """
+        self.history.append(
+            {
+                "role": "user",
+                "content": prompt,
+            }
+        )
+
         instructions = LLMInstructions(norms if norms else None, goals if goals else None)
         messages = [
             {

src/control_backend/agents/mock_agents/test_pause_ri.py

@@ -0,0 +1,68 @@
+import asyncio
+import json
+
+import zmq
+from zmq.asyncio import Context
+
+from control_backend.agents.base import BaseAgent
+from control_backend.core.agent_system import InternalMessage
+from control_backend.core.config import settings
+
+
+class TestPauseAgent(BaseAgent):
+    def __init__(self, name: str):
+        super().__init__(name)
+        
+    async def setup(self):
+        context = Context.instance()
+        self.pub_socket = context.socket(zmq.PUB)
+        self.pub_socket.connect(settings.zmq_settings.internal_pub_address)
+
+        self.add_behavior(self._pause_command_loop())
+        self.logger.debug("TestPauseAgent setup complete.")
+    
+    async def _pause_command_loop(self):
+        print("Starting Pause command test loop.")
+        while True:
+            pause_command = {
+                "endpoint": "pause",
+                "data": True,
+            }
+
+            message = InternalMessage(
+                to="ri_communication_agent",
+                sender=self.name,
+                body=json.dumps(pause_command),
+            )
+            await self.send(message)
+
+            # User interrupt message 
+            data = {
+                "type": "pause",
+                "context": True,
+            }
+            await self.pub_socket.send_multipart([b"button_pressed", json.dumps(data).encode()])
+
+            self.logger.info("Pausing robot actions.")
+            await asyncio.sleep(15)  # Simulate delay between messages
+
+            pause_command = {
+                "endpoint": "pause",
+                "data": False,
+            }
+            message = InternalMessage(
+                to="ri_communication_agent",
+                sender=self.name,
+                body=json.dumps(pause_command),
+            )
+            await self.send(message)
+
+            # User interrupt message
+            data = {
+                "type": "pause",
+                "context": False,
+            }
+            await self.pub_socket.send_multipart([b"button_pressed", json.dumps(data).encode()])
+
+            self.logger.info("Resuming robot actions.")
+            await asyncio.sleep(15)  # Simulate delay between messages

src/control_backend/agents/perception/vad_agent.py

@@ -110,11 +110,12 @@ class VADAgent(BaseAgent):
 
         self._connect_audio_in_socket()
 
-        audio_out_address = self._connect_audio_out_socket()
+        audio_out_port = self._connect_audio_out_socket()
-        if audio_out_address is None:
+        if audio_out_port is None:
             self.logger.error("Could not bind output socket, stopping.")
             await self.stop()
             return
+        audio_out_address = f"tcp://localhost:{audio_out_port}"
 
         # Connect to internal communication socket
         self.program_sub_socket = azmq.Context.instance().socket(zmq.SUB)
@@ -167,14 +168,13 @@ class VADAgent(BaseAgent):
             self.audio_in_socket.connect(self.audio_in_address)
         self.audio_in_poller = SocketPoller[bytes](self.audio_in_socket)
 
-    def _connect_audio_out_socket(self) -> str | None:
+    def _connect_audio_out_socket(self) -> int | None:
         """
-        Returns the address that was bound to, or None if binding failed.
+        Returns the port bound, or None if binding failed.
         """
         try:
             self.audio_out_socket = azmq.Context.instance().socket(zmq.PUB)
-            self.audio_out_socket.bind(settings.zmq_settings.vad_pub_address)
+            return self.audio_out_socket.bind_to_random_port("tcp://localhost", max_tries=100)
-            return settings.zmq_settings.vad_pub_address
         except zmq.ZMQBindError:
             self.logger.error("Failed to bind an audio output socket after 100 tries.")
             self.audio_out_socket = None
@@ -246,11 +246,10 @@ class VADAgent(BaseAgent):
             assert self.model is not None
             prob = self.model(torch.from_numpy(chunk), settings.vad_settings.sample_rate_hz).item()
             non_speech_patience = settings.behaviour_settings.vad_non_speech_patience_chunks
-            begin_silence_length = settings.behaviour_settings.vad_begin_silence_chunks
             prob_threshold = settings.behaviour_settings.vad_prob_threshold
 
             if prob > prob_threshold:
-                if self.i_since_speech > non_speech_patience + begin_silence_length:
+                if self.i_since_speech > non_speech_patience:
                     self.logger.debug("Speech started.")
                 self.audio_buffer = np.append(self.audio_buffer, chunk)
                 self.i_since_speech = 0
@@ -264,7 +263,7 @@ class VADAgent(BaseAgent):
                 continue
 
             # Speech probably ended. Make sure we have a usable amount of data.
-            if len(self.audio_buffer) > begin_silence_length * len(chunk):
+            if len(self.audio_buffer) >= 3 * len(chunk):
                 self.logger.debug("Speech ended.")
                 assert self.audio_out_socket is not None
                 await self.audio_out_socket.send(self.audio_buffer[: -2 * len(chunk)].tobytes())

src/control_backend/agents/user_interrupt/user_interrupt_agent.py

@@ -4,11 +4,8 @@ import zmq
 from zmq.asyncio import Context
 
 from control_backend.agents import BaseAgent
-from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
-from control_backend.schemas.belief_message import Belief, BeliefMessage
-from control_backend.schemas.program import ConditionalNorm, Program
 from control_backend.schemas.ri_message import (
     GestureCommand,
     PauseCommand,
@@ -26,45 +23,18 @@ class UserInterruptAgent(BaseAgent):
 
     - Send a prioritized message to the `RobotSpeechAgent`
     - Send a prioritized gesture to the `RobotGestureAgent`
-    - Send a belief override to the `BDI Core` in order to activate a
+    - Send a belief override to the `BDIProgramManager`in order to activate a
         trigger/conditional norm or complete a goal.
 
     Prioritized actions clear the current RI queue before inserting the new item,
     ensuring they are executed immediately after Pepper's current action has been fulfilled.
 
-    :ivar sub_socket: The ZMQ SUB socket used to receive user interrupts.
+    :ivar sub_socket: The ZMQ SUB socket used to receive user intterupts.
     """
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.sub_socket = None
-        self.pub_socket = None
-        self._trigger_map = {}
-        self._trigger_reverse_map = {}
-
-        self._goal_map = {}  # id -> sluggified goal
-        self._goal_reverse_map = {}  # sluggified goal -> id
-
-        self._cond_norm_map = {}  # id -> sluggified cond norm
-        self._cond_norm_reverse_map = {}  # sluggified cond norm -> id
-
-    async def setup(self):
-        """
-        Initialize the agent.
-
-        Connects the internal ZMQ SUB socket and subscribes to the 'button_pressed' topic.
-        Starts the background behavior to receive the user interrupts.
-        """
-        context = Context.instance()
-
-        self.sub_socket = context.socket(zmq.SUB)
-        self.sub_socket.connect(settings.zmq_settings.internal_sub_address)
-        self.sub_socket.subscribe("button_pressed")
-
-        self.pub_socket = context.socket(zmq.PUB)
-        self.pub_socket.connect(settings.zmq_settings.internal_pub_address)
-
-        self.add_behavior(self._receive_button_event())
 
     async def _receive_button_event(self):
         """
@@ -75,11 +45,7 @@ class UserInterruptAgent(BaseAgent):
         These are the different types and contexts:
         - type: "speech", context: string that the robot has to say.
         - type: "gesture", context: single gesture name that the robot has to perform.
-        - type: "override", context: id that belongs to the goal/trigger/conditional norm.
+        - type: "override", context: belief_id that overrides the goal/trigger/conditional norm.
-        - type: "override_unachieve", context: id that belongs to the conditional norm to unachieve.
-        - type: "next_phase", context: None, indicates to the BDI Core to
-        - type: "pause", context: boolean indicating whether to pause
-        - type: "reset_phase", context: None, indicates to the BDI Core to
         """
         while True:
             topic, body = await self.sub_socket.recv_multipart()
@@ -92,201 +58,37 @@ class UserInterruptAgent(BaseAgent):
                 self.logger.error("Received invalid JSON payload on topic %s", topic)
                 continue
 
-            self.logger.debug("Received event type %s", event_type)
+            if event_type == "speech":
-
-            match event_type:
-                case "speech":
                 await self._send_to_speech_agent(event_context)
                 self.logger.info(
                     "Forwarded button press (speech) with context '%s' to RobotSpeechAgent.",
                     event_context,
                 )
-                case "gesture":
+            elif event_type == "gesture":
                 await self._send_to_gesture_agent(event_context)
                 self.logger.info(
                     "Forwarded button press (gesture) with context '%s' to RobotGestureAgent.",
                     event_context,
                 )
-                case "override":
+            elif event_type == "override":
-                    ui_id = str(event_context)
+                await self._send_to_program_manager(event_context)
-                    if asl_trigger := self._trigger_map.get(ui_id):
-                        await self._send_to_bdi("force_trigger", asl_trigger)
                 self.logger.info(
-                            "Forwarded button press (override) with context '%s' to BDI Core.",
+                    "Forwarded button press (override) with context '%s' to BDIProgramManager.",
                     event_context,
                 )
-                    elif asl_cond_norm := self._cond_norm_map.get(ui_id):
+            elif event_type == "pause":
-                        await self._send_to_bdi_belief(asl_cond_norm, "cond_norm")
-                        self.logger.info(
-                            "Forwarded button press (override) with context '%s' to BDI Core.",
-                            event_context,
-                        )
-                    elif asl_goal := self._goal_map.get(ui_id):
-                        await self._send_to_bdi_belief(asl_goal, "goal")
-                        self.logger.info(
-                            "Forwarded button press (override) with context '%s' to BDI Core.",
-                            event_context,
-                        )
-                        # Send achieve_goal to program manager to update semantic belief extractor
-                        goal_achieve_msg = InternalMessage(
-                            to=settings.agent_settings.bdi_program_manager_name,
-                            thread="achieve_goal",
-                            body=ui_id,
-                        )
-
-                        await self.send(goal_achieve_msg)
-                    else:
-                        self.logger.warning("Could not determine which element to override.")
-                case "override_unachieve":
-                    ui_id = str(event_context)
-                    if asl_cond_norm := self._cond_norm_map.get(ui_id):
-                        await self._send_to_bdi_belief(asl_cond_norm, "cond_norm", True)
-                        self.logger.info(
-                            "Forwarded button press (override_unachieve)"
-                            "with context '%s' to BDI Core.",
-                            event_context,
-                        )
-                    else:
-                        self.logger.warning(
-                            "Could not determine which conditional norm to unachieve."
-                        )
-
-                case "pause":
-                    self.logger.debug(
-                        "Received pause/resume button press with context '%s'.", event_context
-                    )
                 await self._send_pause_command(event_context)
                 if event_context:
                     self.logger.info("Sent pause command.")
                 else:
                     self.logger.info("Sent resume command.")
-
+            else:
-                case "next_phase" | "reset_phase":
-                    await self._send_experiment_control_to_bdi_core(event_type)
-                case _:
                 self.logger.warning(
                     "Received button press with unknown type '%s' (context: '%s').",
                     event_type,
                     event_context,
                 )
 
-    async def handle_message(self, msg: InternalMessage):
-        """
-        Handle commands received from other internal Python agents.
-        """
-        match msg.thread:
-            case "new_program":
-                self._create_mapping(msg.body)
-            case "trigger_start":
-                # msg.body is the sluggified trigger
-                asl_slug = msg.body
-                ui_id = self._trigger_reverse_map.get(asl_slug)
-
-                if ui_id:
-                    payload = {"type": "trigger_update", "id": ui_id, "achieved": True}
-                    await self._send_experiment_update(payload)
-                    self.logger.info(f"UI Update: Trigger {asl_slug} started (ID: {ui_id})")
-            case "trigger_end":
-                asl_slug = msg.body
-                ui_id = self._trigger_reverse_map.get(asl_slug)
-                if ui_id:
-                    payload = {"type": "trigger_update", "id": ui_id, "achieved": False}
-                    await self._send_experiment_update(payload)
-                    self.logger.info(f"UI Update: Trigger {asl_slug} ended (ID: {ui_id})")
-            case "transition_phase":
-                new_phase_id = msg.body
-                self.logger.info(f"Phase transition detected: {new_phase_id}")
-
-                payload = {"type": "phase_update", "id": new_phase_id}
-
-                await self._send_experiment_update(payload)
-            case "goal_start":
-                goal_name = msg.body
-                ui_id = self._goal_reverse_map.get(goal_name)
-                if ui_id:
-                    payload = {"type": "goal_update", "id": ui_id}
-                    await self._send_experiment_update(payload)
-                    self.logger.info(f"UI Update: Goal {goal_name} started (ID: {ui_id})")
-            case "active_norms_update":
-                active_norms_asl = [
-                    s.strip("() '\",") for s in msg.body.split(",") if s.strip("() '\",")
-                ]
-                await self._broadcast_cond_norms(active_norms_asl)
-            case _:
-                self.logger.debug(f"Received internal message on unhandled thread: {msg.thread}")
-
-    async def _broadcast_cond_norms(self, active_slugs: list[str]):
-        """
-        Sends the current state of all conditional norms to the UI.
-        :param active_slugs: A list of slugs (strings) currently active in the BDI core.
-        """
-        updates = []
-        for asl_slug, ui_id in self._cond_norm_reverse_map.items():
-            is_active = asl_slug in active_slugs
-            updates.append({"id": ui_id, "active": is_active})
-
-        payload = {"type": "cond_norms_state_update", "norms": updates}
-
-        if self.pub_socket:
-            topic = b"status"
-            body = json.dumps(payload).encode("utf-8")
-            await self.pub_socket.send_multipart([topic, body])
-            # self.logger.info(f"UI Update: Active norms {updates}")
-
-    def _create_mapping(self, program_json: str):
-        """
-        Create mappings between UI IDs and ASL slugs for triggers, goals, and conditional norms
-        """
-        try:
-            program = Program.model_validate_json(program_json)
-            self._trigger_map = {}
-            self._trigger_reverse_map = {}
-            self._goal_map = {}
-            self._cond_norm_map = {}
-            self._cond_norm_reverse_map = {}
-
-            for phase in program.phases:
-                for trigger in phase.triggers:
-                    slug = AgentSpeakGenerator.slugify(trigger)
-                    self._trigger_map[str(trigger.id)] = slug
-                    self._trigger_reverse_map[slug] = str(trigger.id)
-
-                for goal in phase.goals:
-                    self._goal_map[str(goal.id)] = AgentSpeakGenerator.slugify(goal)
-                    self._goal_reverse_map[AgentSpeakGenerator.slugify(goal)] = str(goal.id)
-
-                for goal, id in self._goal_reverse_map.items():
-                    self.logger.debug(f"Goal mapping: UI ID {goal} -> {id}")
-
-                for norm in phase.norms:
-                    if isinstance(norm, ConditionalNorm):
-                        asl_slug = AgentSpeakGenerator.slugify(norm)
-
-                        norm_id = str(norm.id)
-
-                        self._cond_norm_map[norm_id] = asl_slug
-                        self._cond_norm_reverse_map[norm.norm] = norm_id
-                        self.logger.debug("Added conditional norm %s", asl_slug)
-
-            self.logger.info(
-                f"Mapped {len(self._trigger_map)} triggers and {len(self._goal_map)} goals "
-                f"and {len(self._cond_norm_map)} conditional norms for UserInterruptAgent."
-            )
-        except Exception as e:
-            self.logger.error(f"Mapping failed: {e}")
-
-    async def _send_experiment_update(self, data, should_log: bool = True):
-        """
-        Sends an update to the 'experiment' topic.
-        The SSE endpoint will pick this up and push it to the UI.
-        """
-        if self.pub_socket:
-            topic = b"experiment"
-            body = json.dumps(data).encode("utf-8")
-            await self.pub_socket.send_multipart([topic, body])
-            if should_log:
-                self.logger.debug(f"Sent experiment update: {data}")
-
     async def _send_to_speech_agent(self, text_to_say: str):
         """
         method to send prioritized speech command to RobotSpeechAgent.
@@ -318,62 +120,28 @@ class UserInterruptAgent(BaseAgent):
         )
         await self.send(out_msg)
 
-    async def _send_to_bdi(self, thread: str, body: str):
+    async def _send_to_program_manager(self, belief_id: str):
-        """Send slug of trigger to BDI"""
-        msg = InternalMessage(to=settings.agent_settings.bdi_core_name, thread=thread, body=body)
-        await self.send(msg)
-        self.logger.info(f"Directly forced {thread} in BDI: {body}")
-
-    async def _send_to_bdi_belief(self, asl: str, asl_type: str, unachieve: bool = False):
-        """Send belief to BDI Core"""
-        if asl_type == "goal":
-            belief_name = f"achieved_{asl}"
-        elif asl_type == "cond_norm":
-            belief_name = f"force_{asl}"
-        else:
-            self.logger.warning("Tried to send belief with unknown type")
-        belief = Belief(name=belief_name, arguments=None)
-        self.logger.debug(f"Sending belief to BDI Core: {belief_name}")
-        # Conditional norms are unachieved by removing the belief
-        belief_message = (
-            BeliefMessage(delete=[belief]) if unachieve else BeliefMessage(create=[belief])
-        )
-        msg = InternalMessage(
-            to=settings.agent_settings.bdi_core_name,
-            thread="beliefs",
-            body=belief_message.model_dump_json(),
-        )
-        await self.send(msg)
-
-    async def _send_experiment_control_to_bdi_core(self, type):
         """
-        method to send experiment control buttons to bdi core.
+        Send a button_override belief to the BDIProgramManager.
 
-        :param type: the type of control button we should send to the bdi core.
+        :param belief_id: The belief_id that overrides the goal/trigger/conditional norm.
+        this id can belong to a basic belief or an inferred belief.
+        See also: https://utrechtuniversity.youtrack.cloud/articles/N25B-A-27/UI-components
         """
-        # Switch which thread we should send to bdi core
+        data = {"belief": belief_id}
-        thread = ""
+        message = InternalMessage(
-        match type:
+            to=settings.agent_settings.bdi_program_manager_name,
-            case "next_phase":
-                thread = "force_next_phase"
-            case "reset_phase":
-                thread = "reset_current_phase"
-            case _:
-                self.logger.warning(
-                    "Received unknown experiment control type '%s' to send to BDI Core.",
-                    type,
-                )
-
-        out_msg = InternalMessage(
-            to=settings.agent_settings.bdi_core_name,
             sender=self.name,
-            thread=thread,
+            body=json.dumps(data),
-            body="",
+            thread="belief_override_id",
+        )
+        await self.send(message)
+        self.logger.info(
+            "Sent button_override belief with id '%s' to Program manager.",
+            belief_id,
         )
-        self.logger.debug("Sending experiment control '%s' to BDI Core.", thread)
-        await self.send(out_msg)
 
-    async def _send_pause_command(self, pause):
+    async def _send_pause_command(self, pause : bool):
         """
         Send a pause command to the Robot Interface via the RI Communication Agent.
         Send a pause command to the other internal agents; for now just VAD agent. 
@@ -386,7 +154,7 @@ class UserInterruptAgent(BaseAgent):
         )
         await self.send(message)
 
-        if pause == "true":
+        if pause:
             # Send pause to VAD agent
             vad_message = InternalMessage(
                 to=settings.agent_settings.vad_name,
@@ -404,3 +172,18 @@ class UserInterruptAgent(BaseAgent):
             )
             await self.send(vad_message)
             self.logger.info("Sent resume command to VAD Agent and RI Communication Agent.")
+
+    async def setup(self):
+        """
+        Initialize the agent.
+
+        Connects the internal ZMQ SUB socket and subscribes to the 'button_pressed' topic.
+        Starts the background behavior to receive the user interrupts.
+        """
+        context = Context.instance()
+
+        self.sub_socket = context.socket(zmq.SUB)
+        self.sub_socket.connect(settings.zmq_settings.internal_sub_address)
+        self.sub_socket.subscribe("button_pressed")
+
+        self.add_behavior(self._receive_button_event())

src/control_backend/api/v1/endpoints/button_pressed.py

@@ -0,0 +1,31 @@
+import logging
+
+from fastapi import APIRouter, Request
+
+from control_backend.schemas.events import ButtonPressedEvent
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+
+@router.post("/button_pressed", status_code=202)
+async def receive_button_event(event: ButtonPressedEvent, request: Request):
+    """
+    Endpoint to handle external button press events.
+
+    Validates the event payload and publishes it to the internal 'button_pressed' topic.
+    Subscribers (in this case user_interrupt_agent) will pick this up to trigger
+    specific behaviors or state changes.
+
+    :param event: The parsed ButtonPressedEvent object.
+    :param request: The FastAPI request object.
+    """
+    logger.debug("Received button event: %s | %s", event.type, event.context)
+
+    topic = b"button_pressed"
+    body = event.model_dump_json().encode()
+
+    pub_socket = request.app.state.endpoints_pub_socket
+    await pub_socket.send_multipart([topic, body])
+
+    return {"status": "Event received"}

src/control_backend/api/v1/endpoints/robot.py

@@ -137,6 +137,7 @@ async def ping_stream(request: Request):
                 logger.info("Client disconnected from SSE")
                 break
 
+            logger.debug(f"Yielded new connection event in robot ping router: {str(connected)}")
             connectedJson = json.dumps(connected)
             yield (f"data: {connectedJson}\n\n")
 

src/control_backend/api/v1/endpoints/user_interact.py

@@ -1,94 +0,0 @@
-import asyncio
-import logging
-
-import zmq
-import zmq.asyncio
-from fastapi import APIRouter, Request
-from fastapi.responses import StreamingResponse
-from zmq.asyncio import Context
-
-from control_backend.core.config import settings
-from control_backend.schemas.events import ButtonPressedEvent
-
-logger = logging.getLogger(__name__)
-router = APIRouter()
-
-
-@router.post("/button_pressed", status_code=202)
-async def receive_button_event(event: ButtonPressedEvent, request: Request):
-    """
-    Endpoint to handle external button press events.
-
-    Validates the event payload and publishes it to the internal 'button_pressed' topic.
-    Subscribers (in this case user_interrupt_agent) will pick this up to trigger
-    specific behaviors or state changes.
-
-    :param event: The parsed ButtonPressedEvent object.
-    :param request: The FastAPI request object.
-    """
-    logger.debug("Received button event: %s | %s", event.type, event.context)
-
-    topic = b"button_pressed"
-    body = event.model_dump_json().encode()
-
-    pub_socket = request.app.state.endpoints_pub_socket
-    await pub_socket.send_multipart([topic, body])
-
-    return {"status": "Event received"}
-
-
-@router.get("/experiment_stream")
-async def experiment_stream(request: Request):
-    # Use the asyncio-compatible context
-    context = Context.instance()
-    socket = context.socket(zmq.SUB)
-
-    # Connect and subscribe
-    socket.connect(settings.zmq_settings.internal_sub_address)
-    socket.subscribe(b"experiment")
-
-    async def gen():
-        try:
-            while True:
-                # Check if client closed the tab
-                if await request.is_disconnected():
-                    logger.error("Client disconnected from experiment stream.")
-                    break
-
-                try:
-                    parts = await asyncio.wait_for(socket.recv_multipart(), timeout=10.0)
-                    _, message = parts
-                    yield f"data: {message.decode().strip()}\n\n"
-                except TimeoutError:
-                    continue
-        finally:
-            socket.close()
-
-    return StreamingResponse(gen(), media_type="text/event-stream")
-
-
-@router.get("/status_stream")
-async def status_stream(request: Request):
-    context = Context.instance()
-    socket = context.socket(zmq.SUB)
-    socket.connect(settings.zmq_settings.internal_sub_address)
-
-    socket.subscribe(b"status")
-
-    async def gen():
-        try:
-            while True:
-                if await request.is_disconnected():
-                    break
-                try:
-                    # Shorter timeout since this is frequent
-                    parts = await asyncio.wait_for(socket.recv_multipart(), timeout=0.5)
-                    _, message = parts
-                    yield f"data: {message.decode().strip()}\n\n"
-                except TimeoutError:
-                    yield ": ping\n\n"  # Keep the connection alive
-                    continue
-        finally:
-            socket.close()
-
-    return StreamingResponse(gen(), media_type="text/event-stream")

src/control_backend/api/v1/router.py

@@ -1,6 +1,6 @@
 from fastapi.routing import APIRouter
 
-from control_backend.api.v1.endpoints import logs, message, program, robot, sse, user_interact
+from control_backend.api.v1.endpoints import button_pressed, logs, message, program, robot, sse
 
 api_router = APIRouter()
 
@@ -14,4 +14,4 @@ api_router.include_router(logs.router, tags=["Logs"])
 
 api_router.include_router(program.router, tags=["Program"])
 
-api_router.include_router(user_interact.router, tags=["Button Pressed Events"])
+api_router.include_router(button_pressed.router, tags=["Button Pressed Events"])

src/control_backend/core/agent_system.py

@@ -60,9 +60,6 @@ class BaseAgent(ABC):
         self._tasks: set[asyncio.Task] = set()
         self._running = False
 
-        self._internal_pub_socket: None | azmq.Socket = None
-        self._internal_sub_socket: None | azmq.Socket = None
-
         # Register immediately
         AgentDirectory.register(name, self)
 
@@ -120,7 +117,7 @@ class BaseAgent(ABC):
             task.cancel()
         self.logger.info(f"Agent {self.name} stopped")
 
-    async def send(self, message: InternalMessage, should_log: bool = True):
+    async def send(self, message: InternalMessage):
         """
         Send a message to another agent.
 
@@ -133,25 +130,15 @@ class BaseAgent(ABC):
 
         :param message: The message to send.
         """
-        message.sender = self.name
+        target = AgentDirectory.get(message.to)
-        to = message.to
-        receivers = [to] if isinstance(to, str) else to
-
-        for receiver in receivers:
-            target = AgentDirectory.get(receiver)
-
         if target:
             await target.inbox.put(message)
-                if should_log:
+            self.logger.debug(f"Sent message {message.body} to {message.to} via regular inbox.")
-                    self.logger.debug(
-                        f"Sent message {message.body} to {message.to} via regular inbox."
-                    )
         else:
             # Apparently target agent is on a different process, send via ZMQ
-                topic = f"internal/{receiver}".encode()
+            topic = f"internal/{message.to}".encode()
             body = message.model_dump_json().encode()
             await self._internal_pub_socket.send_multipart([topic, body])
-                if should_log:
             self.logger.debug(f"Sent message {message.body} to {message.to} via ZMQ.")
 
     async def _process_inbox(self):
@@ -162,6 +149,7 @@ class BaseAgent(ABC):
         """
         while self._running:
             msg = await self.inbox.get()
+            self.logger.debug(f"Received message from {msg.sender}.")
             await self.handle_message(msg)
 
     async def _receive_internal_zmq_loop(self):
@@ -204,16 +192,7 @@ class BaseAgent(ABC):
 
         :param coro: The coroutine to execute as a task.
         """
-
+        task = asyncio.create_task(coro)
-        async def try_coro(coro_: Coroutine):
-            try:
-                await coro_
-            except asyncio.CancelledError:
-                self.logger.debug("A behavior was canceled successfully: %s", coro_)
-            except Exception:
-                self.logger.warning("An exception occurred in a behavior.", exc_info=True)
-
-        task = asyncio.create_task(try_coro(coro))
         self._tasks.add(task)
         task.add_done_callback(self._tasks.discard)
         return task

src/control_backend/core/config.py

@@ -1,12 +1,3 @@
-"""
-An exhaustive overview of configurable options. All of these can be set using environment variables
-by nesting with double underscores (__). Start from the ``Settings`` class.
-
-For example, ``settings.ri_host`` becomes ``RI_HOST``, and
-``settings.zmq_settings.ri_communication_address`` becomes
-``ZMQ_SETTINGS__RI_COMMUNICATION_ADDRESS``.
-"""
-
 from pydantic import BaseModel
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
@@ -17,17 +8,16 @@ class ZMQSettings(BaseModel):
 
     :ivar internal_pub_address: Address for the internal PUB socket.
     :ivar internal_sub_address: Address for the internal SUB socket.
-    :ivar ri_communication_address: Address for the endpoint that the Robot Interface connects to.
+    :ivar ri_command_address: Address for sending commands to the Robot Interface.
-    :ivar vad_pub_address: Address that the VAD agent binds to and publishes audio segments to.
+    :ivar ri_communication_address: Address for receiving communication from the Robot Interface.
+    :ivar vad_agent_address: Address for the Voice Activity Detection (VAD) agent.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     internal_pub_address: str = "tcp://localhost:5560"
     internal_sub_address: str = "tcp://localhost:5561"
+    ri_command_address: str = "tcp://localhost:0000"
     ri_communication_address: str = "tcp://*:5555"
     internal_gesture_rep_adress: str = "tcp://localhost:7788"
-    vad_pub_address: str = "inproc://vad_stream"
 
 
 class AgentSettings(BaseModel):
@@ -46,8 +36,6 @@ class AgentSettings(BaseModel):
     :ivar robot_speech_name: Name of the Robot Speech Agent.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     # agent names
     bdi_core_name: str = "bdi_core_agent"
     bdi_belief_collector_name: str = "belief_collector_agent"
@@ -73,7 +61,6 @@ class BehaviourSettings(BaseModel):
     :ivar vad_prob_threshold: Probability threshold for Voice Activity Detection.
     :ivar vad_initial_since_speech: Initial value for 'since speech' counter in VAD.
     :ivar vad_non_speech_patience_chunks: Number of non-speech chunks to wait before speech ended.
-    :ivar vad_begin_silence_chunks: The number of chunks of silence to prepend to speech chunks.
     :ivar transcription_max_concurrent_tasks: Maximum number of concurrent transcription tasks.
     :ivar transcription_words_per_minute: Estimated words per minute for transcription timing.
     :ivar transcription_words_per_token: Estimated words per token for transcription timing.
@@ -81,8 +68,6 @@ class BehaviourSettings(BaseModel):
     :ivar conversation_history_length_limit: The maximum amount of messages to extract beliefs from.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     sleep_s: float = 1.0
     comm_setup_max_retries: int = 5
     socket_poller_timeout_ms: int = 100
@@ -90,8 +75,7 @@ class BehaviourSettings(BaseModel):
     # VAD settings
     vad_prob_threshold: float = 0.5
     vad_initial_since_speech: int = 100
-    vad_non_speech_patience_chunks: int = 15
+    vad_non_speech_patience_chunks: int = 3
-    vad_begin_silence_chunks: int = 6
 
     # transcription behaviour
     transcription_max_concurrent_tasks: int = 3
@@ -115,8 +99,6 @@ class LLMSettings(BaseModel):
     :ivar n_parallel: The number of parallel calls allowed to be made to the LLM.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     local_llm_url: str = "http://localhost:1234/v1/chat/completions"
     local_llm_model: str = "gpt-oss"
     chat_temperature: float = 1.0
@@ -133,8 +115,6 @@ class VADSettings(BaseModel):
     :ivar sample_rate_hz: Sample rate in Hz for the VAD model.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     repo_or_dir: str = "snakers4/silero-vad"
     model_name: str = "silero_vad"
     sample_rate_hz: int = 16000
@@ -148,8 +128,6 @@ class SpeechModelSettings(BaseModel):
     :ivar openai_model_name: Model name for OpenAI-based speech recognition.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     # model identifiers for speech recognition
     mlx_model_name: str = "mlx-community/whisper-small.en-mlx"
     openai_model_name: str = "small.en"
@@ -161,7 +139,6 @@ class Settings(BaseSettings):
 
     :ivar app_title: Title of the application.
     :ivar ui_url: URL of the frontend UI.
-    :ivar ri_host: The hostname of the Robot Interface.
     :ivar zmq_settings: ZMQ configuration.
     :ivar agent_settings: Agent name configuration.
     :ivar behaviour_settings: Behavior configuration.
@@ -174,8 +151,6 @@ class Settings(BaseSettings):
 
     ui_url: str = "http://localhost:5173"
 
-    ri_host: str = "localhost"
-
     zmq_settings: ZMQSettings = ZMQSettings()
 
     agent_settings: AgentSettings = AgentSettings()

src/control_backend/main.py

@@ -39,10 +39,11 @@ from control_backend.agents.communication import RICommunicationAgent
 # LLM Agents
 from control_backend.agents.llm import LLMAgent
 
+# Other backend imports
+from control_backend.agents.mock_agents.test_pause_ri import TestPauseAgent
+
 # User Interrupt Agent
 from control_backend.agents.user_interrupt.user_interrupt_agent import UserInterruptAgent
-
-# Other backend imports
 from control_backend.api.v1.router import api_router
 from control_backend.core.config import settings
 from control_backend.logging import setup_logging
@@ -120,6 +121,7 @@ async def lifespan(app: FastAPI):
             BDICoreAgent,
             {
                 "name": settings.agent_settings.bdi_core_name,
+                "asl": "src/control_backend/agents/bdi/rules.asl",
             },
         ),
         "BeliefCollectorAgent": (
@@ -140,6 +142,12 @@ async def lifespan(app: FastAPI):
                 "name": settings.agent_settings.bdi_program_manager_name,
             },
         ),
+        "TestPauseAgent": (
+            TestPauseAgent,
+            {
+                "name": "pause_test_agent",
+            },
+        ),
         "UserInterruptAgent": (
             UserInterruptAgent,
             {

src/control_backend/schemas/belief_list.py

@@ -1,19 +0,0 @@
-from pydantic import BaseModel
-
-from control_backend.schemas.program import BaseGoal
-from control_backend.schemas.program import Belief as ProgramBelief
-
-
-class BeliefList(BaseModel):
-    """
-    Represents a list of beliefs, separated from a program. Useful in agents which need to
-    communicate beliefs.
-
-    :ivar: beliefs: The list of beliefs.
-    """
-
-    beliefs: list[ProgramBelief]
-
-
-class GoalList(BaseModel):
-    goals: list[BaseGoal]

src/control_backend/schemas/belief_message.py

@@ -11,10 +11,7 @@ class Belief(BaseModel):
     """
 
     name: str
-    arguments: list[str] | None = None
+    arguments: list[str] | None
-
-    # To make it hashable
-    model_config = {"frozen": True}
 
 
 class BeliefMessage(BaseModel):

src/control_backend/schemas/internal_message.py

@@ -1,5 +1,3 @@
-from collections.abc import Iterable
-
 from pydantic import BaseModel
 
 
@@ -13,7 +11,7 @@ class InternalMessage(BaseModel):
     :ivar thread: An optional thread identifier/topic to categorize the message (e.g., 'beliefs').
     """
 
-    to: str | Iterable[str]
+    to: str
-    sender: str | None = None
+    sender: str
     body: str
     thread: str | None = None

src/control_backend/schemas/program.py

@@ -15,9 +15,6 @@ class ProgramElement(BaseModel):
     name: str
     id: UUID4
 
-    # To make program elements hashable
-    model_config = {"frozen": True}
-
 
 class LogicalOperator(Enum):
     AND = "AND"
@@ -46,6 +43,7 @@ class SemanticBelief(ProgramElement):
     :ivar description: Description of how to form the belief, used by the LLM.
     """
 
+    name: str = ""
     description: str
 
 
@@ -108,31 +106,19 @@ class Plan(ProgramElement):
     steps: list[PlanElement]
 
 
-class BaseGoal(ProgramElement):
+class Goal(ProgramElement):
     """
-    Represents an objective to be achieved. This base version does not include a plan to achieve
+    Represents an objective to be achieved. To reach the goal, we should execute
-    this goal, and is used in semantic belief extraction.
+    the corresponding plan. If we can fail to achieve a goal after executing the plan,
+    for example when the achieving of the goal is dependent on the user's reply, this means
+    that the achieved status will be set from somewhere else in the program.
 
-    :ivar description: A description of the goal, used to determine if it has been achieved.
+    :ivar plan: The plan to execute.
     :ivar can_fail: Whether we can fail to achieve the goal after executing the plan.
     """
 
-    description: str = ""
-    can_fail: bool = True
-
-
-class Goal(BaseGoal):
-    """
-    Represents an objective to be achieved. To reach the goal, we should execute the corresponding
-    plan. It inherits from the BaseGoal a variable `can_fail`, which if true will cause the
-    completion to be determined based on the conversation.
-
-    Instances of this goal are not hashable because a plan is not hashable.
-
-    :ivar plan: The plan to execute.
-    """
-
     plan: Plan
+    can_fail: bool = True
 
 
 type Action = SpeechAction | GestureAction | LLMAction
@@ -193,6 +179,7 @@ class Trigger(ProgramElement):
     :ivar plan: The plan to execute.
     """
 
+    name: str = ""
     condition: Belief
     plan: Plan
 

src/control_backend/schemas/ri_message.py

@@ -14,7 +14,7 @@ class RIEndpoint(str, Enum):
     GESTURE_TAG = "actuate/gesture/tag"
     PING = "ping"
     NEGOTIATE_PORTS = "negotiate/ports"
-    PAUSE = ""
+    PAUSE = "pause"
 
 
 class RIMessage(BaseModel):
@@ -66,7 +66,6 @@ class GestureCommand(RIMessage):
             raise ValueError("endpoint must be GESTURE_SINGLE or GESTURE_TAG")
         return self
 
-
 class PauseCommand(RIMessage):
     """
     A specific command to pause or unpause the robot's actions.

test/integration/agents/perception/vad_agent/test_vad_agent.py

@@ -91,7 +91,7 @@ def test_out_socket_creation(zmq_context):
     assert per_vad_agent.audio_out_socket is not None
 
     zmq_context.return_value.socket.assert_called_once_with(zmq.PUB)
-    zmq_context.return_value.socket.return_value.bind.assert_called_once_with("inproc://vad_stream")
+    zmq_context.return_value.socket.return_value.bind_to_random_port.assert_called_once()
 
 
 @pytest.mark.asyncio

test/unit/agents/actuation/test_robot_gesture_agent.py

@@ -73,7 +73,7 @@ async def test_setup_connect(zmq_context, mocker):
 async def test_handle_message_sends_valid_gesture_command():
     """Internal message with valid gesture tag is forwarded to robot pub socket."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     payload = {
@@ -91,7 +91,7 @@ async def test_handle_message_sends_valid_gesture_command():
 async def test_handle_message_sends_non_gesture_command():
     """Internal message with non-gesture endpoint is not forwarded by this agent."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     payload = {"endpoint": "some_other_endpoint", "data": "invalid_tag_not_in_list"}
@@ -107,7 +107,7 @@ async def test_handle_message_sends_non_gesture_command():
 async def test_handle_message_rejects_invalid_gesture_tag():
     """Internal message with invalid gesture tag is not forwarded."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     # Use a tag that's not in gesture_data
@@ -123,7 +123,7 @@ async def test_handle_message_rejects_invalid_gesture_tag():
 async def test_handle_message_invalid_payload():
     """Invalid payload is caught and does not send."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     msg = InternalMessage(to="robot", sender="tester", body=json.dumps({"bad": "data"}))
@@ -142,12 +142,12 @@ async def test_zmq_command_loop_valid_gesture_payload():
     async def recv_once():
         # stop after first iteration
         agent._running = False
-        return b"command", json.dumps(command).encode("utf-8")
+        return (b"command", json.dumps(command).encode("utf-8"))
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -165,12 +165,12 @@ async def test_zmq_command_loop_valid_non_gesture_payload():
 
     async def recv_once():
         agent._running = False
-        return b"command", json.dumps(command).encode("utf-8")
+        return (b"command", json.dumps(command).encode("utf-8"))
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -188,12 +188,12 @@ async def test_zmq_command_loop_invalid_gesture_tag():
 
     async def recv_once():
         agent._running = False
-        return b"command", json.dumps(command).encode("utf-8")
+        return (b"command", json.dumps(command).encode("utf-8"))
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -210,12 +210,12 @@ async def test_zmq_command_loop_invalid_json():
 
     async def recv_once():
         agent._running = False
-        return b"command", b"{not_json}"
+        return (b"command", b"{not_json}")
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -232,12 +232,12 @@ async def test_zmq_command_loop_ignores_send_gestures_topic():
 
     async def recv_once():
         agent._running = False
-        return b"send_gestures", b"{}"
+        return (b"send_gestures", b"{}")
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -259,9 +259,7 @@ async def test_fetch_gestures_loop_without_amount():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent(
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"])
-        "robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"], address=""
-    )
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -289,9 +287,7 @@ async def test_fetch_gestures_loop_with_amount():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent(
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"])
-        "robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"], address=""
-    )
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -319,7 +315,7 @@ async def test_fetch_gestures_loop_with_integer_request():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -344,7 +340,7 @@ async def test_fetch_gestures_loop_with_invalid_json():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -369,7 +365,7 @@ async def test_fetch_gestures_loop_with_non_integer_json():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -385,7 +381,7 @@ async def test_fetch_gestures_loop_with_non_integer_json():
 def test_gesture_data_attribute():
     """Test that gesture_data returns the expected list."""
     gesture_data = ["hello", "yes", "no", "wave"]
-    agent = RobotGestureAgent("robot_gesture", gesture_data=gesture_data, address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=gesture_data)
 
     assert agent.gesture_data == gesture_data
     assert isinstance(agent.gesture_data, list)
@@ -402,7 +398,7 @@ async def test_stop_closes_sockets():
     pubsocket = MagicMock()
     subsocket = MagicMock()
     repsocket = MagicMock()
-    agent = RobotGestureAgent("robot_gesture", address="")
+    agent = RobotGestureAgent("robot_gesture")
     agent.pubsocket = pubsocket
     agent.subsocket = subsocket
     agent.repsocket = repsocket
@@ -419,7 +415,7 @@ async def test_stop_closes_sockets():
 async def test_initialization_with_custom_gesture_data():
     """Agent can be initialized with custom gesture data."""
     custom_gestures = ["custom1", "custom2", "custom3"]
-    agent = RobotGestureAgent("robot_gesture", gesture_data=custom_gestures, address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=custom_gestures)
 
     assert agent.gesture_data == custom_gestures
 
@@ -436,7 +432,7 @@ async def test_fetch_gestures_loop_handles_exception():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent.logger = MagicMock()
     agent._running = True

test/unit/agents/bdi/test_bdi_core_agent.py

@@ -20,7 +20,7 @@ def mock_agentspeak_env():
 
 @pytest.fixture
 def agent():
-    agent = BDICoreAgent("bdi_agent")
+    agent = BDICoreAgent("bdi_agent", "dummy.asl")
     agent.send = AsyncMock()
     agent.bdi_agent = MagicMock()
     return agent
@@ -133,14 +133,14 @@ async def test_custom_actions(agent):
 
     # Invoke action
     mock_term = MagicMock()
-    mock_term.args = ["Hello", "Norm"]
+    mock_term.args = ["Hello", "Norm", "Goal"]
     mock_intention = MagicMock()
 
     # Run generator
     gen = action_fn(agent, mock_term, mock_intention)
     next(gen)  # Execute
 
-    agent._send_to_llm.assert_called_with("Hello", "Norm", "")
+    agent._send_to_llm.assert_called_with("Hello", "Norm", "Goal")
 
 
 def test_add_belief_sets_event(agent):

test/unit/agents/bdi/test_bdi_program_manager.py

@@ -32,8 +32,6 @@ def make_valid_program_json(norm="N1", goal="G1") -> str:
                     Goal(
                         id=uuid.uuid4(),
                         name=goal,
-                        description="This description can be used to determine whether the goal "
-                        "has been achieved.",
                         plan=Plan(
                             id=uuid.uuid4(),
                             name="Goal Plan",
@@ -55,7 +53,7 @@ async def test_send_to_bdi():
     manager.send = AsyncMock()
 
     program = Program.model_validate_json(make_valid_program_json())
-    await manager._create_agentspeak_and_send_to_bdi(program)
+    await manager._send_to_bdi(program)
 
     assert manager.send.await_count == 1
     msg: InternalMessage = manager.send.await_args[0][0]
@@ -77,10 +75,8 @@ async def test_receive_programs_valid_and_invalid():
     ]
 
     manager = BDIProgramManager(name="program_manager_test")
-    manager._internal_pub_socket = AsyncMock()
     manager.sub_socket = sub
-    manager._create_agentspeak_and_send_to_bdi = AsyncMock()
+    manager._send_to_bdi = AsyncMock()
-    manager._send_clear_llm_history = AsyncMock()
 
     try:
         # Will give StopAsyncIteration when the predefined `sub.recv_multipart` side-effects run out
@@ -89,30 +85,7 @@ async def test_receive_programs_valid_and_invalid():
         pass
 
     # Only valid Program should have triggered _send_to_bdi
-    assert manager._create_agentspeak_and_send_to_bdi.await_count == 1
+    assert manager._send_to_bdi.await_count == 1
-    forwarded: Program = manager._create_agentspeak_and_send_to_bdi.await_args[0][0]
+    forwarded: Program = manager._send_to_bdi.await_args[0][0]
     assert forwarded.phases[0].norms[0].name == "N1"
     assert forwarded.phases[0].goals[0].name == "G1"
-
-    # Verify history clear was triggered
-    assert (
-        manager._send_clear_llm_history.await_count == 2
-    )  # first sends program to UserInterrupt, then clears LLM
-
-
-@pytest.mark.asyncio
-async def test_send_clear_llm_history(mock_settings):
-    # Ensure the mock returns a string for the agent name (just like in your LLM tests)
-    mock_settings.agent_settings.llm_agent_name = "llm_agent"
-
-    manager = BDIProgramManager(name="program_manager_test")
-    manager.send = AsyncMock()
-
-    await manager._send_clear_llm_history()
-
-    assert manager.send.await_count == 2
-    msg: InternalMessage = manager.send.await_args_list[0][0][0]
-
-    # Verify the content and recipient
-    assert msg.body == "clear_history"
-    assert msg.to == "llm_agent"

test/unit/agents/bdi/test_text_belief_extractor.py

@@ -6,16 +6,11 @@ import httpx
 import pytest
 
 from control_backend.agents.bdi import TextBeliefExtractorAgent
-from control_backend.agents.bdi.text_belief_extractor_agent import BeliefState
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
-from control_backend.schemas.belief_list import BeliefList
-from control_backend.schemas.belief_message import Belief as InternalBelief
 from control_backend.schemas.belief_message import BeliefMessage
-from control_backend.schemas.chat_history import ChatHistory, ChatMessage
 from control_backend.schemas.program import (
     ConditionalNorm,
-    KeywordBelief,
     LLMAction,
     Phase,
     Plan,
@@ -26,20 +21,10 @@ from control_backend.schemas.program import (
 
 
 @pytest.fixture
-def llm():
+def agent():
-    llm = TextBeliefExtractorAgent.LLM(MagicMock(), 4)
-    llm._query_llm = AsyncMock()
-    return llm
-
-
-@pytest.fixture
-def agent(llm):
-    with patch(
-        "control_backend.agents.bdi.text_belief_extractor_agent.TextBeliefExtractorAgent.LLM",
-        return_value=llm,
-    ):
     agent = TextBeliefExtractorAgent("text_belief_agent")
     agent.send = AsyncMock()
+    agent._query_llm = AsyncMock()
     return agent
 
 
@@ -115,12 +100,24 @@ async def test_handle_message_from_transcriber(agent, mock_settings):
 
     agent.send.assert_awaited_once()  # noqa  # `agent.send` has no such property, but we mock it.
     sent: InternalMessage = agent.send.call_args.args[0]  # noqa
-    assert sent.to == mock_settings.agent_settings.bdi_core_name
+    assert sent.to == mock_settings.agent_settings.bdi_belief_collector_name
     assert sent.thread == "beliefs"
-    parsed = BeliefMessage.model_validate_json(sent.body)
+    parsed = json.loads(sent.body)
-    replaced_last = parsed.replace.pop()
+    assert parsed == {"beliefs": {"user_said": [transcription]}, "type": "belief_extraction_text"}
-    assert replaced_last.name == "user_said"
+
-    assert replaced_last.arguments == [transcription]
+
+@pytest.mark.asyncio
+async def test_process_user_said(agent, mock_settings):
+    transcription = "this is a test"
+
+    await agent._user_said(transcription)
+
+    agent.send.assert_awaited_once()  # noqa  # `agent.send` has no such property, but we mock it.
+    sent: InternalMessage = agent.send.call_args.args[0]  # noqa
+    assert sent.to == mock_settings.agent_settings.bdi_belief_collector_name
+    assert sent.thread == "beliefs"
+    parsed = json.loads(sent.body)
+    assert parsed["beliefs"]["user_said"] == [transcription]
 
 
 @pytest.mark.asyncio
@@ -145,97 +142,77 @@ async def test_query_llm():
         "control_backend.agents.bdi.text_belief_extractor_agent.httpx.AsyncClient",
         return_value=mock_async_client,
     ):
-        llm = TextBeliefExtractorAgent.LLM(MagicMock(), 4)
+        agent = TextBeliefExtractorAgent("text_belief_agent")
 
-        res = await llm._query_llm("hello world", {"type": "null"})
+        res = await agent._query_llm("hello world", {"type": "null"})
         # Response content was set as "null", so should be deserialized as None
         assert res is None
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_success(llm):
+async def test_retry_query_llm_success(agent):
-    llm._query_llm.return_value = None
+    agent._query_llm.return_value = None
-    res = await llm.query("hello world", {"type": "null"})
+    res = await agent._retry_query_llm("hello world", {"type": "null"})
 
-    llm._query_llm.assert_called_once()
+    agent._query_llm.assert_called_once()
     assert res is None
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_success_after_failure(llm):
+async def test_retry_query_llm_success_after_failure(agent):
-    llm._query_llm.side_effect = [KeyError(), "real value"]
+    agent._query_llm.side_effect = [KeyError(), "real value"]
-    res = await llm.query("hello world", {"type": "string"})
+    res = await agent._retry_query_llm("hello world", {"type": "string"})
 
-    assert llm._query_llm.call_count == 2
+    assert agent._query_llm.call_count == 2
     assert res == "real value"
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_failures(llm):
+async def test_retry_query_llm_failures(agent):
-    llm._query_llm.side_effect = [KeyError(), KeyError(), KeyError(), "real value"]
+    agent._query_llm.side_effect = [KeyError(), KeyError(), KeyError(), "real value"]
-    res = await llm.query("hello world", {"type": "string"})
+    res = await agent._retry_query_llm("hello world", {"type": "string"})
 
-    assert llm._query_llm.call_count == 3
+    assert agent._query_llm.call_count == 3
     assert res is None
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_fail_immediately(llm):
+async def test_retry_query_llm_fail_immediately(agent):
-    llm._query_llm.side_effect = [KeyError(), "real value"]
+    agent._query_llm.side_effect = [KeyError(), "real value"]
-    res = await llm.query("hello world", {"type": "string"}, tries=1)
+    res = await agent._retry_query_llm("hello world", {"type": "string"}, tries=1)
 
-    assert llm._query_llm.call_count == 1
+    assert agent._query_llm.call_count == 1
     assert res is None
 
 
 @pytest.mark.asyncio
-async def test_extracting_semantic_beliefs(agent):
+async def test_extracting_beliefs_from_program(agent, sample_program):
-    """
+    assert len(agent.available_beliefs) == 0
-    The Program Manager sends beliefs to this agent. Test whether the agent handles them correctly.
-    """
-    assert len(agent.belief_inferrer.available_beliefs) == 0
-    beliefs = BeliefList(
-        beliefs=[
-            KeywordBelief(
-                id=uuid.uuid4(),
-                name="keyword_hello",
-                keyword="hello",
-            ),
-            SemanticBelief(
-                id=uuid.uuid4(), name="semantic_hello_1", description="Some semantic belief 1"
-            ),
-            SemanticBelief(
-                id=uuid.uuid4(), name="semantic_hello_2", description="Some semantic belief 2"
-            ),
-        ]
-    )
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
             sender=settings.agent_settings.bdi_program_manager_name,
-            body=beliefs.model_dump_json(),
+            body=sample_program.model_dump_json(),
-            thread="beliefs",
         ),
     )
-    assert len(agent.belief_inferrer.available_beliefs) == 2
+    assert len(agent.available_beliefs) == 2
 
 
 @pytest.mark.asyncio
-async def test_handle_invalid_beliefs(agent, sample_program):
+async def test_handle_invalid_program(agent, sample_program):
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    assert len(agent.belief_inferrer.available_beliefs) == 2
+    assert len(agent.available_beliefs) == 2
 
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
             sender=settings.agent_settings.bdi_program_manager_name,
             body=json.dumps({"phases": "Invalid"}),
-            thread="beliefs",
         ),
     )
 
-    assert len(agent.belief_inferrer.available_beliefs) == 2
+    assert len(agent.available_beliefs) == 2
 
 
 @pytest.mark.asyncio
@@ -257,13 +234,13 @@ async def test_handle_robot_response(agent):
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_with_beliefs(agent, llm, sample_program):
+async def test_simulated_real_turn_with_beliefs(agent, sample_program):
     """Test sending user message to extract beliefs from."""
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
 
     # Send a user message with the belief that there's no more booze
-    llm._query_llm.return_value = {"is_pirate": None, "no_more_booze": True}
+    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": True}
     assert len(agent.conversation.messages) == 0
     await agent.handle_message(
         InternalMessage(
@@ -278,20 +255,20 @@ async def test_simulated_real_turn_with_beliefs(agent, llm, sample_program):
     assert agent.send.call_count == 2
 
     # First should be the beliefs message
-    message: InternalMessage = agent.send.call_args_list[1].args[0]
+    message: InternalMessage = agent.send.call_args_list[0].args[0]
     beliefs = BeliefMessage.model_validate_json(message.body)
     assert len(beliefs.create) == 1
     assert beliefs.create[0].name == "no_more_booze"
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_no_beliefs(agent, llm, sample_program):
+async def test_simulated_real_turn_no_beliefs(agent, sample_program):
     """Test a user message to extract beliefs from, but no beliefs are formed."""
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
 
     # Send a user message with no new beliefs
-    llm._query_llm.return_value = {"is_pirate": None, "no_more_booze": None}
+    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": None}
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
@@ -305,17 +282,17 @@ async def test_simulated_real_turn_no_beliefs(agent, llm, sample_program):
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_no_new_beliefs(agent, llm, sample_program):
+async def test_simulated_real_turn_no_new_beliefs(agent, sample_program):
     """
     Test a user message to extract beliefs from, but no new beliefs are formed because they already
     existed.
     """
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    agent._current_beliefs = BeliefState(true={InternalBelief(name="is_pirate", arguments=None)})
+    agent.beliefs["is_pirate"] = True
 
     # Send a user message with the belief the user is a pirate, still
-    llm._query_llm.return_value = {"is_pirate": True, "no_more_booze": None}
+    agent._query_llm.return_value = {"is_pirate": True, "no_more_booze": None}
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
@@ -329,19 +306,17 @@ async def test_simulated_real_turn_no_new_beliefs(agent, llm, sample_program):
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_remove_belief(agent, llm, sample_program):
+async def test_simulated_real_turn_remove_belief(agent, sample_program):
     """
     Test a user message to extract beliefs from, but an existing belief is determined no longer to
     hold.
     """
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    agent._current_beliefs = BeliefState(
+    agent.beliefs["no_more_booze"] = True
-        true={InternalBelief(name="no_more_booze", arguments=None)},
-    )
 
     # Send a user message with the belief the user is a pirate, still
-    llm._query_llm.return_value = {"is_pirate": None, "no_more_booze": False}
+    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": False}
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
@@ -354,23 +329,18 @@ async def test_simulated_real_turn_remove_belief(agent, llm, sample_program):
     assert agent.send.call_count == 2
 
     # Agent's current beliefs should've changed
-    assert any(b.name == "no_more_booze" for b in agent._current_beliefs.false)
+    assert not agent.beliefs["no_more_booze"]
 
 
 @pytest.mark.asyncio
-async def test_llm_failure_handling(agent, llm, sample_program):
+async def test_llm_failure_handling(agent, sample_program):
     """
     Check that the agent handles failures gracefully without crashing.
     """
-    llm._query_llm.side_effect = httpx.HTTPError("")
+    agent._query_llm.side_effect = httpx.HTTPError("")
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
 
-    belief_changes = await agent.belief_inferrer.infer_from_conversation(
+    belief_changes = await agent._infer_turn()
-        ChatHistory(
-            messages=[ChatMessage(role="user", content="Good day!")],
-        ),
-    )
 
-    assert len(belief_changes.true) == 0
+    assert len(belief_changes) == 0
-    assert len(belief_changes.false) == 0

test/unit/agents/llm/test_llm_agent.py

@@ -265,23 +265,3 @@ async def test_stream_query_llm_skips_non_data_lines(mock_httpx_client, mock_set
 
     # Only the valid 'data:' line should yield content
     assert tokens == ["Hi"]
-
-
-@pytest.mark.asyncio
-async def test_clear_history_command(mock_settings):
-    """Test that the 'clear_history' message clears the agent's memory."""
-    # setup LLM to have some history
-    mock_settings.agent_settings.bdi_program_manager_name = "bdi_program_manager_agent"
-    agent = LLMAgent("llm_agent")
-    agent.history = [
-        {"role": "user", "content": "Old conversation context"},
-        {"role": "assistant", "content": "Old response"},
-    ]
-    assert len(agent.history) == 2
-    msg = InternalMessage(
-        to="llm_agent",
-        sender=mock_settings.agent_settings.bdi_program_manager_name,
-        body="clear_history",
-    )
-    await agent.handle_message(msg)
-    assert len(agent.history) == 0

test/unit/agents/perception/vad_agent/test_vad_streaming.py

@@ -7,15 +7,6 @@ import zmq
 from control_backend.agents.perception.vad_agent import VADAgent
 
 
-# We don't want to use real ZMQ in unit tests, for example because it can give errors when sockets
-# aren't closed properly.
-@pytest.fixture(autouse=True)
-def mock_zmq():
-    with patch("zmq.asyncio.Context") as mock:
-        mock.instance.return_value = MagicMock()
-        yield mock
-
-
 @pytest.fixture
 def audio_out_socket():
     return AsyncMock()
@@ -149,10 +140,12 @@ async def test_vad_model_load_failure_stops_agent(vad_agent):
         # Patch stop to an AsyncMock so we can check it was awaited
         vad_agent.stop = AsyncMock()
 
-        await vad_agent.setup()
+        result = await vad_agent.setup()
 
         # Assert stop was called
         vad_agent.stop.assert_awaited_once()
+        # Assert setup returned None
+        assert result is None
 
 
 @pytest.mark.asyncio
@@ -162,7 +155,7 @@ async def test_audio_out_bind_failure_sets_none_and_logs(vad_agent, caplog):
     audio_out_socket is set to None, None is returned, and an error is logged.
     """
     mock_socket = MagicMock()
-    mock_socket.bind.side_effect = zmq.ZMQBindError()
+    mock_socket.bind_to_random_port.side_effect = zmq.ZMQBindError()
     with patch("control_backend.agents.perception.vad_agent.azmq.Context.instance") as mock_ctx:
         mock_ctx.return_value.socket.return_value = mock_socket
 

test/unit/api/v1/endpoints/test_program_endpoint.py

@@ -43,8 +43,6 @@ def make_valid_program_dict():
                     Goal(
                         id=uuid.uuid4(),
                         name="Some goal",
-                        description="This description can be used to determine whether the goal "
-                        "has been achieved.",
                         plan=Plan(
                             id=uuid.uuid4(),
                             name="Goal Plan",

test/unit/core/test_agent_system.py

@@ -99,75 +99,12 @@ async def test_send_to_local_agent(monkeypatch):
     # Patch inbox.put
     target.inbox.put = AsyncMock()
 
-    message = InternalMessage(to=target.name, sender=sender.name, body="hello")
+    message = InternalMessage(to="receiver", sender="sender", body="hello")
 
     await sender.send(message)
 
     target.inbox.put.assert_awaited_once_with(message)
-
+    sender.logger.debug.assert_called_once()
-
-@pytest.mark.asyncio
-async def test_send_to_zmq_agent(monkeypatch):
-    sender = DummyAgent("sender")
-    target = "remote_receiver"
-
-    # Fake logger
-    sender.logger = MagicMock()
-
-    # Fake zmq
-    sender._internal_pub_socket = AsyncMock()
-
-    message = InternalMessage(to=target, sender=sender.name, body="hello")
-
-    await sender.send(message)
-
-    zmq_calls = sender._internal_pub_socket.send_multipart.call_args[0][0]
-    assert zmq_calls[0] == f"internal/{target}".encode()
-
-
-@pytest.mark.asyncio
-async def test_send_to_multiple_local_agents(monkeypatch):
-    sender = DummyAgent("sender")
-    target1 = DummyAgent("receiver1")
-    target2 = DummyAgent("receiver2")
-
-    # Fake logger
-    sender.logger = MagicMock()
-
-    # Patch inbox.put
-    target1.inbox.put = AsyncMock()
-    target2.inbox.put = AsyncMock()
-
-    message = InternalMessage(to=[target1.name, target2.name], sender=sender.name, body="hello")
-
-    await sender.send(message)
-
-    target1.inbox.put.assert_awaited_once_with(message)
-    target2.inbox.put.assert_awaited_once_with(message)
-
-
-@pytest.mark.asyncio
-async def test_send_to_multiple_agents(monkeypatch):
-    sender = DummyAgent("sender")
-    target1 = DummyAgent("receiver1")
-    target2 = "remote_receiver"
-
-    # Fake logger
-    sender.logger = MagicMock()
-
-    # Fake zmq
-    sender._internal_pub_socket = AsyncMock()
-
-    # Patch inbox.put
-    target1.inbox.put = AsyncMock()
-
-    message = InternalMessage(to=[target1.name, target2], sender=sender.name, body="hello")
-
-    await sender.send(message)
-
-    target1.inbox.put.assert_awaited_once_with(message)
-    zmq_calls = sender._internal_pub_socket.send_multipart.call_args[0][0]
-    assert zmq_calls[0] == f"internal/{target2}".encode()
 
 
 @pytest.mark.asyncio

test/unit/schemas/test_ui_program_message.py

@@ -31,7 +31,6 @@ def base_goal() -> Goal:
     return Goal(
         id=uuid.uuid4(),
         name="testGoalName",
-        description="This description can be used to determine whether the goal has been achieved.",
         plan=Plan(
             id=uuid.uuid4(),
             name="testGoalPlanName",