feat: make the pipeline work with Program and AgentSpeak

ref: N25B-429

.gitlab/merge_request_templates/default.md

@@ -1,9 +0,0 @@
-%{first_multiline_commit_description}
-
-To verify:
-
-- [ ] Style checks pass
-- [ ] Pipeline (tests) pass
-- [ ] Documentation is up to date
-- [ ] Tests are up to date (new code is covered)
-- [ ] ...

src/control_backend/agents/actuation/robot_gesture_agent.py

@@ -28,7 +28,6 @@ class RobotGestureAgent(BaseAgent):
     address = ""
     bind = False
     gesture_data = []
-    single_gesture_data = []
 
     def __init__(
         self,
@@ -36,10 +35,8 @@ class RobotGestureAgent(BaseAgent):
         address=settings.zmq_settings.ri_command_address,
         bind=False,
         gesture_data=None,
-        single_gesture_data=None,
     ):
         self.gesture_data = gesture_data or []
-        self.single_gesture_data = single_gesture_data or []
         super().__init__(name)
         self.address = address
         self.bind = bind
@@ -102,13 +99,7 @@ class RobotGestureAgent(BaseAgent):
                         gesture_command.data,
                     )
                     return
-            elif gesture_command.endpoint == RIEndpoint.GESTURE_SINGLE:
+
-                if gesture_command.data not in self.single_gesture_data:
-                    self.logger.warning(
-                        "Received gesture '%s' which is not in available gestures. Early returning",
-                        gesture_command.data,
-                    )
-                    return
             await self.pubsocket.send_json(gesture_command.model_dump())
         except Exception:
             self.logger.exception("Error processing internal message.")

src/control_backend/agents/bdi/agentspeak_ast.py

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

src/control_backend/agents/bdi/agentspeak_generator.py

@@ -0,0 +1,373 @@
+from functools import singledispatchmethod
+
+from slugify import slugify
+
+from control_backend.agents.bdi.agentspeak_ast import (
+    AstBinaryOp,
+    AstExpression,
+    AstLiteral,
+    AstPlan,
+    AstProgram,
+    AstRule,
+    AstStatement,
+    AstString,
+    AstVar,
+    BinaryOperatorType,
+    StatementType,
+    TriggerType,
+)
+from control_backend.schemas.program import (
+    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()
+
+        self._asp.rules.append(AstRule(self._astify(program.phases[0])))
+        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()
+
+    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 _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.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)
+            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")])
+        )
+
+        context = [from_phase_ast, ~AstLiteral("responded_this_turn")]
+        if from_phase and from_phase.goals:
+            context.append(self._astify(from_phase.goals[-1], achieved=True))
+
+        body = [
+            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")])
+                    ),
+                ]
+            )
+
+        self._asp.plans.append(
+            AstPlan(TriggerType.ADDED_GOAL, AstLiteral("transition_phase"), 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))
+            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.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,
+    ) -> 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 = []
+
+        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 = []
+
+        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)
+
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                AstLiteral("check_triggers"),
+                [self._astify(phase), self._astify(trigger.condition)],
+                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"))],
+            )
+        )
+
+    @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(f"semantic_{self._slugify_str(sb.description)}")
+
+    @_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, 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)])
+
+    @staticmethod
+    def _slugify_str(text: str) -> str:
+        return slugify(text, separator="_", stopwords=["a", "an", "the", "we", "you", "I"])

src/control_backend/agents/bdi/bdi_core_agent.py

@@ -11,7 +11,7 @@ from pydantic import ValidationError
 from control_backend.agents.base import BaseAgent
 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.belief_message import Belief, BeliefMessage
 from control_backend.schemas.llm_prompt_message import LLMPromptMessage
 from control_backend.schemas.ri_message import SpeechCommand
 
@@ -42,13 +42,13 @@ class BDICoreAgent(BaseAgent):
 
     bdi_agent: agentspeak.runtime.Agent
 
-    def __init__(self, name: str, asl: str):
+    def __init__(self, name: 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:
         """
@@ -65,19 +65,22 @@ class BDICoreAgent(BaseAgent):
         await self._load_asl()
 
         # Start the BDI cycle loop
-        self.add_behavior(self._bdi_loop())
+        self._bdi_loop_task = self.add_behavior(self._bdi_loop())
         self._wake_bdi_loop.set()
         self.logger.debug("Setup complete.")
 
-    async def _load_asl(self):
+    async def _load_asl(self, file_name: str | None = None) -> None:
         """
         Load and parse the AgentSpeak source file.
         """
+        file_name = file_name or "src/control_backend/agents/bdi/default_behavior.asl"
+
         try:
-            with open(self.asl_file) as source:
+            with open(file_name) 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 {self.asl_file}.")
+            self.logger.warning(f"Could not find the specified ASL file at {file_name}.")
             self.bdi_agent = agentspeak.runtime.Agent(self.env, self.name)
 
     async def _bdi_loop(self):
@@ -116,6 +119,7 @@ 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.
@@ -124,12 +128,19 @@ class BDICoreAgent(BaseAgent):
 
         if msg.thread == "beliefs":
             try:
-                belief_changes = BeliefMessage.model_validate_json(msg.body)
+                beliefs = BeliefMessage.model_validate_json(msg.body).beliefs
-                self._apply_belief_changes(belief_changes)
+                self._apply_beliefs(beliefs)
             except ValidationError:
                 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:
@@ -145,28 +156,21 @@ class BDICoreAgent(BaseAgent):
                 )
                 await self.send(out_msg)
 
-    def _apply_belief_changes(self, belief_changes: BeliefMessage):
+    def _apply_beliefs(self, beliefs: list[Belief]):
         """
         Update the belief base with a list of new beliefs.
 
-        For beliefs in ``belief_changes.replace``, it removes all existing beliefs with that name
+        If ``replace=True`` is set on a belief, it removes all existing beliefs with that name
-        before adding one new one.
+        before adding the new one.
-
-        :param belief_changes: The changes in beliefs to apply.
         """
-        if not belief_changes.create and not belief_changes.replace and not belief_changes.delete:
+        if not beliefs:
             return
 
-        for belief in belief_changes.create:
+        for belief in beliefs:
+            if belief.replace:
+                self._remove_all_with_name(belief.name)
             self._add_belief(belief.name, belief.arguments)
 
-        for belief in belief_changes.replace:
-            self._remove_all_with_name(belief.name)
-            self._add_belief(belief.name, belief.arguments)
-
-        for belief in belief_changes.delete:
-            self._remove_belief(belief.name, belief.arguments)
-
     def _add_belief(self, name: str, args: list[str] = None):
         """
         Add a single belief to the BDI agent.
@@ -246,20 +250,18 @@ class BDICoreAgent(BaseAgent):
         the function expects (which will be located in `term.args`).
         """
 
-        @self.actions.add(".reply", 3)
+        @self.actions.add(".reply", 2)
         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.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)))
+            self.add_behavior(self._send_to_llm(str(message_text), str(norms), ""))
             yield
 
         @self.actions.add(".reply_with_goal", 3)
@@ -278,7 +280,7 @@ class BDICoreAgent(BaseAgent):
                 norms,
                 goal,
             )
-            # asyncio.create_task(self._send_to_llm(str(message_text), norms, str(goal)))
+            self.add_behavior(self._send_to_llm(str(message_text), str(norms), str(goal)))
             yield
 
         @self.actions.add(".say", 1)
@@ -290,13 +292,14 @@ 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))
+            # TODO: add to conversation history
+            self.add_behavior(self.send(speech_message))
             yield
 
         @self.actions.add(".gesture", 2)

src/control_backend/agents/bdi/bdi_program_manager.py

@@ -1,598 +1,12 @@
-import uuid
-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.program import (
+from control_backend.schemas.internal_message import InternalMessage
-    Action,
+from control_backend.schemas.program import Program
-    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):
@@ -611,40 +25,36 @@ class BDIProgramManager(BaseAgent):
         super().__init__(**kwargs)
         self.sub_socket = None
 
-    # async def _send_to_bdi(self, program: Program):
+    async def _create_agentspeak_and_send_to_bdi(self, program: Program):
-    #     """
+        """
-    #     Convert a received program into BDI beliefs and send them to the BDI Core Agent.
+        Convert a received program into BDI beliefs and send them to the BDI Core Agent.
-    #
+
-    #     Currently, it takes the **first phase** of the program and extracts:
+        Currently, it takes the **first phase** of the program and extracts:
-    #     -   **Norms**: Constraints or rules the agent must follow.
+        -   **Norms**: Constraints or rules the agent must follow.
-    #     -   **Goals**: Objectives the agent must achieve.
+        -   **Goals**: Objectives the agent must achieve.
-    #
+
-    #     These are sent as a ``BeliefMessage`` with ``replace=True``, meaning they will
+        These are sent as a ``BeliefMessage`` with ``replace=True``, meaning they will
-    #     overwrite any existing norms/goals of the same name in the BDI agent.
+        overwrite any existing norms/goals of the same name in the BDI agent.
-    #
+
-    #     :param program: The program object received from the API.
+        :param program: The program object received from the API.
-    #     """
+        """
-    #     first_phase = program.phases[0]
+        asg = AgentSpeakGenerator()
-    #     norms_belief = Belief(
+
-    #         name="norms",
+        asl_str = asg.generate(program)
-    #         arguments=[norm.norm for norm in first_phase.norms],
+
-    #         replace=True,
+        file_name = "src/control_backend/agents/bdi/agentspeak.asl"
-    #     )
+
-    #     goals_belief = Belief(
+        with open(file_name, "w") as f:
-    #         name="goals",
+            f.write(asl_str)
-    #         arguments=[goal.description for goal in first_phase.goals],
+
-    #         replace=True,
+        msg = InternalMessage(
-    #     )
+            sender=self.name,
-    #     program_beliefs = BeliefMessage(beliefs=[norms_belief, goals_belief])
+            to=settings.agent_settings.bdi_core_name,
-    #
+            body=file_name,
-    #     message = InternalMessage(
+            thread="new_program",
-    #         to=settings.agent_settings.bdi_core_name,
+        )
-    #         sender=self.name,
+
-    #         body=program_beliefs.model_dump_json(),
+        await self.send(msg)
-    #         thread="beliefs",
-    #     )
-    #     await self.send(message)
-    #     self.logger.debug("Sent new norms and goals to the BDI agent.")
 
     async def _receive_programs(self):
         """
@@ -662,7 +72,7 @@ class BDIProgramManager(BaseAgent):
                 self.logger.exception("Received an invalid program.")
                 continue
 
-            await self._send_to_bdi(program)
+            await self._create_agentspeak_and_send_to_bdi(program)
 
     async def setup(self):
         """
@@ -678,7 +88,3 @@ class BDIProgramManager(BaseAgent):
         self.sub_socket.subscribe("program")
 
         self.add_behavior(self._receive_programs())
-
-
-if __name__ == "__main__":
-    do_things()

src/control_backend/agents/bdi/belief_collector_agent.py

@@ -101,7 +101,7 @@ class BDIBeliefCollectorAgent(BaseAgent):
             :return: A Belief object if the input is valid or None.
             """
             try:
-                return Belief(name=name, arguments=arguments)
+                return Belief(name=name, arguments=arguments, replace=name == "user_said")
             except ValidationError:
                 return None
 
@@ -144,7 +144,7 @@ class BDIBeliefCollectorAgent(BaseAgent):
         msg = InternalMessage(
             to=settings.agent_settings.bdi_core_name,
             sender=self.name,
-            body=BeliefMessage(create=beliefs).model_dump_json(),
+            body=BeliefMessage(beliefs=beliefs).model_dump_json(),
             thread="beliefs",
         )
 

src/control_backend/agents/bdi/default_behavior.asl

@@ -0,0 +1,5 @@
+norms("").
+
++user_said(Message) : norms(Norms) <-
+  -user_said(Message);
+  .reply(Message, Norms).

src/control_backend/agents/bdi/rules.asl

@@ -1,6 +0,0 @@
-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

@@ -1,23 +1,8 @@
-import asyncio
 import json
 
-import httpx
-from pydantic import ValidationError
-from slugify import slugify
-
 from control_backend.agents.base import BaseAgent
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
-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 (
-    Belief,
-    ConditionalNorm,
-    InferredBelief,
-    Program,
-    SemanticBelief,
-)
 
 
 class TextBeliefExtractorAgent(BaseAgent):
@@ -27,110 +12,46 @@ class TextBeliefExtractorAgent(BaseAgent):
     This agent is responsible for processing raw text (e.g., from speech transcription) and
     extracting semantic beliefs from it.
 
-    It uses the available beliefs received from the program manager to try to extract beliefs from a
+    In the current demonstration version, it performs a simple wrapping of the user's input
-    user's message, sends and updated beliefs to the BDI core, and forms a ``user_said`` belief from
+    into a ``user_said`` belief. In a full implementation, this agent would likely interact
-    the message itself.
+    with an LLM or NLU engine to extract intent, entities, and other structured information.
     """
 
-    def __init__(self, name: str):
-        super().__init__(name)
-        self.beliefs: dict[str, bool] = {}
-        self.available_beliefs: list[SemanticBelief] = []
-        self.conversation = ChatHistory(messages=[])
-
     async def setup(self):
         """
         Initialize the agent and its resources.
         """
-        self.logger.info("Setting up %s.", self.name)
+        self.logger.info("Settting up %s.", self.name)
+        # Setup LLM belief context if needed (currently demo is just passthrough)
+        self.beliefs = {"mood": ["X"], "car": ["Y"]}
 
     async def handle_message(self, msg: InternalMessage):
         """
-        Handle incoming messages. Expect messages from the Transcriber agent, LLM agent, and the
+        Handle incoming messages, primarily from the Transcription Agent.
-        Program manager agent.
 
-        :param msg: The received message.
+        :param msg: The received message containing transcribed text.
         """
         sender = msg.sender
+        if sender == settings.agent_settings.transcription_name:
+            self.logger.debug("Received text from transcriber: %s", msg.body)
+            await self._process_transcription_demo(msg.body)
+        else:
+            self.logger.info("Discarding message from %s", sender)
 
-        match sender:
+    async def _process_transcription_demo(self, txt: str):
-            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._infer_new_beliefs()
-                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:
-                self._handle_program_manager_message(msg)
-            case _:
-                self.logger.info("Discarding message from %s", sender)
-                return
-
-    def _apply_conversation_message(self, message: ChatMessage):
         """
-        Save the chat message to our conversation history, taking into account the conversation
+        Process the transcribed text and generate beliefs.
-        length limit.
 
-        :param message: The chat message to add to the conversation history.
+        **Demo Implementation:**
+        Currently, this method takes the raw text ``txt`` and wraps it into a belief structure:
+        ``user_said("txt")``.
+
+        This belief is then sent to the :class:`BDIBeliefCollectorAgent`.
+
+        :param txt: The raw transcribed text string.
         """
-        length_limit = settings.behaviour_settings.conversation_history_length_limit
+        # For demo, just wrapping user text as user_said belief
-        self.conversation.messages = (self.conversation.messages + [message])[-length_limit:]
+        belief = {"beliefs": {"user_said": [txt]}, "type": "belief_extraction_text"}
-
-    def _handle_program_manager_message(self, msg: InternalMessage):
-        """
-        Handle a message from the program manager: extract available beliefs from it.
-
-        :param msg: The received message from the program manager.
-        """
-        try:
-            program = Program.model_validate_json(msg.body)
-        except ValidationError:
-            self.logger.warning(
-                "Received message from program manager but it is not a valid program."
-            )
-            return
-
-        self.logger.debug("Received a program from the program manager.")
-
-        self.available_beliefs = self._extract_basic_beliefs_from_program(program)
-
-    # TODO Copied from an incomplete version of the program manager. Use that one instead.
-    @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
-                    )
-
-            for trigger in phase.triggers:
-                beliefs += TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(
-                    trigger.condition
-                )
-
-        return beliefs
-
-    # TODO Copied from an incomplete version of the program manager. Use that one instead.
-    @staticmethod
-    def _extract_basic_beliefs_from_belief(belief: Belief) -> list[SemanticBelief]:
-        if isinstance(belief, InferredBelief):
-            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):
-        """
-        Create a belief for the user's full speech.
-
-        :param text: User's transcribed text.
-        """
-        belief = {"beliefs": {"user_said": [text]}, "type": "belief_extraction_text"}
         payload = json.dumps(belief)
 
         belief_msg = InternalMessage(
@@ -139,207 +60,6 @@ class TextBeliefExtractorAgent(BaseAgent):
             body=payload,
             thread="beliefs",
         )
+
         await self.send(belief_msg)
-
+        self.logger.info("Sent %d beliefs to the belief collector.", len(belief["beliefs"]))
-    async def _infer_new_beliefs(self):
-        """
-        Process conversation history to extract beliefs, semantically. Any changed beliefs are sent
-        to the BDI core.
-        """
-        # Return instantly if there are no beliefs to infer
-        if not self.available_beliefs:
-            return
-
-        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
-
-            self.beliefs[belief_key] = belief_value
-
-            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(beliefs_message)
-
-    @staticmethod
-    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)]
-
-    async def _infer_turn(self) -> dict:
-        """
-        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)}
-"""
-
-        schema = self._create_beliefs_schema(beliefs)
-
-        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.
-        :return: An instance of a dict conforming to this schema, or None if failed.
-        """
-        try_count = 0
-        while try_count < tries:
-            try_count += 1
-
-            try:
-                return await self._query_llm(prompt, schema)
-            except (httpx.HTTPError, json.JSONDecodeError, KeyError) as e:
-                if try_count < tries:
-                    continue
-                self.logger.exception(
-                    "Failed to get LLM response after %d tries.",
-                    try_count,
-                    exc_info=e,
-                )
-
-        return None
-
-    @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 to
-        that schema.
-
-        :param prompt: The prompt to be queried.
-        :param schema: Schema to use during response.
-        :return: A dict conforming to this schema.
-        :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 invalid.
-        """
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                settings.llm_settings.local_llm_url,
-                json={
-                    "model": settings.llm_settings.local_llm_model,
-                    "messages": [{"role": "user", "content": prompt}],
-                    "response_format": {
-                        "type": "json_schema",
-                        "json_schema": {
-                            "name": "Beliefs",
-                            "strict": True,
-                            "schema": schema,
-                        },
-                    },
-                    "reasoning_effort": "low",
-                    "temperature": settings.llm_settings.code_temperature,
-                    "stream": False,
-                },
-                timeout=None,
-            )
-            response.raise_for_status()
-
-            response_json = response.json()
-            json_message = response_json["choices"][0]["message"]["content"]
-            return json.loads(json_message)

src/control_backend/agents/communication/ri_communication_agent.py

@@ -3,14 +3,11 @@ import json
 
 import zmq
 import zmq.asyncio as azmq
-from pydantic import ValidationError
 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.ri_message import PauseCommand
 
 from ..actuation.robot_speech_agent import RobotSpeechAgent
 from ..perception import VADAgent
@@ -185,7 +182,6 @@ class RICommunicationAgent(BaseAgent):
                             self._req_socket.bind(addr)
                 case "actuation":
                     gesture_data = port_data.get("gestures", [])
-                    single_gesture_data = port_data.get("single_gestures", [])
                     robot_speech_agent = RobotSpeechAgent(
                         settings.agent_settings.robot_speech_name,
                         address=addr,
@@ -196,7 +192,6 @@ class RICommunicationAgent(BaseAgent):
                         address=addr,
                         bind=bind,
                         gesture_data=gesture_data,
-                        single_gesture_data=single_gesture_data,
                     )
                     await robot_speech_agent.start()
                     await asyncio.sleep(0.1)  # Small delay
@@ -301,11 +296,3 @@ class RICommunicationAgent(BaseAgent):
         self.logger.debug("Restarting communication negotiation.")
         if await self._negotiate_connection(max_retries=1):
             self.connected = True
-
-    async def handle_message(self, msg : InternalMessage):
-        try:
-            pause_command = PauseCommand.model_validate_json(msg.body)
-            self._req_socket.send_json(pause_command.model_dump())
-            self.logger.debug(self._req_socket.recv_json())
-        except ValidationError:
-            self.logger.warning("Incorrect message format for PauseCommand.")

src/control_backend/agents/llm/llm_agent.py

@@ -64,12 +64,11 @@ class LLMAgent(BaseAgent):
 
         :param message: The parsed prompt message containing text, norms, and goals.
         """
-        full_message = ""
         async for chunk in self._query_llm(message.text, message.norms, message.goals):
             await self._send_reply(chunk)
-            full_message += chunk
+            self.logger.debug(
-        self.logger.debug("Finished processing BDI message. Response sent in chunks to BDI core.")
+                "Finished processing BDI message. Response sent in chunks to BDI core."
-        await self._send_full_reply(full_message)
+            )
 
     async def _send_reply(self, msg: str):
         """
@@ -84,19 +83,6 @@ class LLMAgent(BaseAgent):
         )
         await self.send(reply)
 
-    async def _send_full_reply(self, msg: str):
-        """
-        Sends a response message (full) to agents that need it.
-
-        :param msg: The text content of the message.
-        """
-        message = InternalMessage(
-            to=settings.agent_settings.text_belief_extractor_name,
-            sender=self.name,
-            body=msg,
-        )
-        await self.send(message)
-
     async def _query_llm(
         self, prompt: str, norms: list[str], goals: list[str]
     ) -> AsyncGenerator[str]:
@@ -186,7 +172,7 @@ class LLMAgent(BaseAgent):
                 json={
                     "model": settings.llm_settings.local_llm_model,
                     "messages": messages,
-                    "temperature": settings.llm_settings.chat_temperature,
+                    "temperature": 0.3,
                     "stream": True,
                 },
             ) as response:

src/control_backend/agents/mock_agents/test_pause_ri.py

@@ -1,68 +0,0 @@
-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

@@ -7,7 +7,6 @@ import zmq.asyncio as azmq
 
 from control_backend.agents import BaseAgent
 from control_backend.core.config import settings
-from control_backend.schemas.internal_message import InternalMessage
 
 from ...schemas.program_status import PROGRAM_STATUS, ProgramStatus
 from .transcription_agent.transcription_agent import TranscriptionAgent
@@ -87,12 +86,6 @@ class VADAgent(BaseAgent):
         self.audio_buffer = np.array([], dtype=np.float32)
         self.i_since_speech = settings.behaviour_settings.vad_initial_since_speech
         self._ready = asyncio.Event()
-
-        # Pause control
-        self._reset_needed = False
-        self._paused = asyncio.Event()
-        self._paused.set()  # Not paused at start
-
         self.model = None
 
     async def setup(self):
@@ -220,16 +213,6 @@ class VADAgent(BaseAgent):
         """
         await self._ready.wait()
         while self._running:
-            await self._paused.wait()
-
-            # After being unpaused, reset stream and buffers
-            if self._reset_needed:
-                self.logger.debug("Resuming: resetting stream and buffers.")
-                await self._reset_stream()
-                self.audio_buffer = np.array([], dtype=np.float32)
-                self.i_since_speech = settings.behaviour_settings.vad_initial_since_speech
-                self._reset_needed = False
-
             assert self.audio_in_poller is not None
             data = await self.audio_in_poller.poll()
             if data is None:
@@ -271,27 +254,3 @@ class VADAgent(BaseAgent):
             # At this point, we know that the speech has ended.
             # Prepend the last chunk that had no speech, for a more fluent boundary
             self.audio_buffer = chunk
-
-    async def handle_message(self, msg: InternalMessage):
-        """
-        Handle incoming messages.
-
-        Expects messages to pause or resume the VAD processing from User Interrupt Agent.
-
-        :param msg: The received internal message.
-        """
-        sender = msg.sender
-
-        if sender == settings.agent_settings.user_interrupt_name:
-            if msg.body == "PAUSE":
-                self.logger.info("Pausing VAD processing.")
-                self._paused.clear()
-                # If the robot needs to pick up speaking where it left off, do not set _reset_needed
-                self._reset_needed = True
-            elif msg.body == "RESUME":
-                self.logger.info("Resuming VAD processing.")
-                self._paused.set()
-            else:
-                self.logger.warning(f"Unknown command from User Interrupt Agent: {msg.body}")
-        else:
-            self.logger.debug(f"Ignoring message from unknown sender: {sender}")

src/control_backend/agents/user_interrupt/user_interrupt_agent.py

@@ -1,189 +0,0 @@
-import json
-
-import zmq
-from zmq.asyncio import Context
-
-from control_backend.agents import BaseAgent
-from control_backend.core.agent_system import InternalMessage
-from control_backend.core.config import settings
-from control_backend.schemas.ri_message import (
-    GestureCommand,
-    PauseCommand,
-    RIEndpoint,
-    SpeechCommand,
-)
-
-
-class UserInterruptAgent(BaseAgent):
-    """
-    User Interrupt Agent.
-
-    This agent receives button_pressed events from the external HTTP API
-    (via ZMQ) and uses the associated context to trigger one of the following actions:
-
-    - Send a prioritized message to the `RobotSpeechAgent`
-    - Send a prioritized gesture to the `RobotGestureAgent`
-    - 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 intterupts.
-    """
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.sub_socket = None
-
-    async def _receive_button_event(self):
-        """
-        The behaviour of the UserInterruptAgent.
-        Continuous loop that receives button_pressed events from the button_pressed HTTP endpoint.
-        These events contain a type and a context.
-
-        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: belief_id that overrides the goal/trigger/conditional norm.
-        """
-        while True:
-            topic, body = await self.sub_socket.recv_multipart()
-
-            try:
-                event_data = json.loads(body)
-                event_type = event_data.get("type")  # e.g., "speech", "gesture"
-                event_context = event_data.get("context")  # e.g., "Hello, I am Pepper!"
-            except json.JSONDecodeError:
-                self.logger.error("Received invalid JSON payload on topic %s", topic)
-                continue
-
-            if event_type == "speech":
-                await self._send_to_speech_agent(event_context)
-                self.logger.info(
-                    "Forwarded button press (speech) with context '%s' to RobotSpeechAgent.",
-                    event_context,
-                )
-            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,
-                )
-            elif event_type == "override":
-                await self._send_to_program_manager(event_context)
-                self.logger.info(
-                    "Forwarded button press (override) with context '%s' to BDIProgramManager.",
-                    event_context,
-                )
-            elif event_type == "pause":
-                await self._send_pause_command(event_context)
-                if event_context:
-                    self.logger.info("Sent pause command.")
-                else:
-                    self.logger.info("Sent resume command.")
-            else:
-                self.logger.warning(
-                    "Received button press with unknown type '%s' (context: '%s').",
-                    event_type,
-                    event_context,
-                )
-
-    async def _send_to_speech_agent(self, text_to_say: str):
-        """
-        method to send prioritized speech command to RobotSpeechAgent.
-
-        :param text_to_say: The string that the robot has to say.
-        """
-        cmd = SpeechCommand(data=text_to_say, is_priority=True)
-        out_msg = InternalMessage(
-            to=settings.agent_settings.robot_speech_name,
-            sender=self.name,
-            body=cmd.model_dump_json(),
-        )
-        await self.send(out_msg)
-
-    async def _send_to_gesture_agent(self, single_gesture_name: str):
-        """
-        method to send prioritized gesture command to RobotGestureAgent.
-
-        :param single_gesture_name: The gesture tag that the robot has to perform.
-        """
-        # the endpoint is set to always be GESTURE_SINGLE for user interrupts
-        cmd = GestureCommand(
-            endpoint=RIEndpoint.GESTURE_SINGLE, data=single_gesture_name, is_priority=True
-        )
-        out_msg = InternalMessage(
-            to=settings.agent_settings.robot_gesture_name,
-            sender=self.name,
-            body=cmd.model_dump_json(),
-        )
-        await self.send(out_msg)
-
-    async def _send_to_program_manager(self, belief_id: str):
-        """
-        Send a button_override belief to the BDIProgramManager.
-
-        :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
-        """
-        data = {"belief": belief_id}
-        message = InternalMessage(
-            to=settings.agent_settings.bdi_program_manager_name,
-            sender=self.name,
-            body=json.dumps(data),
-            thread="belief_override_id",
-        )
-        await self.send(message)
-        self.logger.info(
-            "Sent button_override belief with id '%s' to Program manager.",
-            belief_id,
-        )
-
-    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. 
-        """
-        cmd = PauseCommand(data=pause)
-        message = InternalMessage(
-            to=settings.agent_settings.ri_communication_name,
-            sender=self.name,
-            body=cmd.model_dump_json(),
-        )
-        await self.send(message)
-
-        if pause:
-            # Send pause to VAD agent
-            vad_message = InternalMessage(
-                to=settings.agent_settings.vad_name,
-                sender=self.name,
-                body="PAUSE",
-            )
-            await self.send(vad_message)
-            self.logger.info("Sent pause command to VAD Agent and RI Communication Agent.")
-        else:
-            # Send resume to VAD agent
-            vad_message = InternalMessage(
-                to=settings.agent_settings.vad_name,
-                sender=self.name,
-                body="RESUME",
-            )
-            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

@@ -1,31 +0,0 @@
-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/router.py

@@ -1,6 +1,6 @@
 from fastapi.routing import APIRouter
 
-from control_backend.api.v1.endpoints import button_pressed, logs, message, program, robot, sse
+from control_backend.api.v1.endpoints import logs, message, program, robot, sse
 
 api_router = APIRouter()
 
@@ -13,5 +13,3 @@ api_router.include_router(robot.router, prefix="/robot", tags=["Pings", "Command
 api_router.include_router(logs.router, tags=["Logs"])
 
 api_router.include_router(program.router, tags=["Program"])
-
-api_router.include_router(button_pressed.router, tags=["Button Pressed Events"])

src/control_backend/core/config.py

@@ -48,7 +48,6 @@ class AgentSettings(BaseModel):
     ri_communication_name: str = "ri_communication_agent"
     robot_speech_name: str = "robot_speech_agent"
     robot_gesture_name: str = "robot_gesture_agent"
-    user_interrupt_name: str = "user_interrupt_agent"
 
 
 class BehaviourSettings(BaseModel):
@@ -65,7 +64,6 @@ class BehaviourSettings(BaseModel):
     :ivar transcription_words_per_minute: Estimated words per minute for transcription timing.
     :ivar transcription_words_per_token: Estimated words per token for transcription timing.
     :ivar transcription_token_buffer: Buffer for transcription tokens.
-    :ivar conversation_history_length_limit: The maximum amount of messages to extract beliefs from.
     """
 
     sleep_s: float = 1.0
@@ -83,9 +81,6 @@ class BehaviourSettings(BaseModel):
     transcription_words_per_token: float = 0.75  # (3 words = 4 tokens)
     transcription_token_buffer: int = 10
 
-    # Text belief extractor settings
-    conversation_history_length_limit: int = 10
-
 
 class LLMSettings(BaseModel):
     """
@@ -93,17 +88,10 @@ class LLMSettings(BaseModel):
 
     :ivar local_llm_url: URL for the local LLM API.
     :ivar local_llm_model: Name of the local LLM model to use.
-    :ivar chat_temperature: The temperature to use while generating chat responses.
-    :ivar code_temperature: The temperature to use while generating code-like responses like during
-        belief inference.
-    :ivar n_parallel: The number of parallel calls allowed to be made to the LLM.
     """
 
     local_llm_url: str = "http://localhost:1234/v1/chat/completions"
     local_llm_model: str = "gpt-oss"
-    chat_temperature: float = 1.0
-    code_temperature: float = 0.3
-    n_parallel: int = 4
 
 
 class VADSettings(BaseModel):

src/control_backend/main.py

@@ -40,10 +40,6 @@ from control_backend.agents.communication import RICommunicationAgent
 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
 from control_backend.api.v1.router import api_router
 from control_backend.core.config import settings
 from control_backend.logging import setup_logging
@@ -121,7 +117,6 @@ async def lifespan(app: FastAPI):
             BDICoreAgent,
             {
                 "name": settings.agent_settings.bdi_core_name,
-                "asl": "src/control_backend/agents/bdi/rules.asl",
             },
         ),
         "BeliefCollectorAgent": (
@@ -142,18 +137,6 @@ async def lifespan(app: FastAPI):
                 "name": settings.agent_settings.bdi_program_manager_name,
             },
         ),
-        "TestPauseAgent": (
-            TestPauseAgent,
-            {
-                "name": "pause_test_agent",
-            },
-        ),
-        "UserInterruptAgent": (
-            UserInterruptAgent,
-            {
-                "name": settings.agent_settings.user_interrupt_name,
-            },
-        ),
     }
 
     agents = []

src/control_backend/schemas/belief_message.py

@@ -6,27 +6,18 @@ class Belief(BaseModel):
     Represents a single belief in the BDI system.
 
     :ivar name: The functor or name of the belief (e.g., 'user_said').
-    :ivar arguments: A list of string arguments for the belief, or None if the belief has no
+    :ivar arguments: A list of string arguments for the belief.
-        arguments.
+    :ivar replace: If True, existing beliefs with this name should be replaced by this one.
     """
 
     name: str
-    arguments: list[str] | None
+    arguments: list[str]
+    replace: bool = False
 
 
 class BeliefMessage(BaseModel):
     """
-    A container for communicating beliefs between agents.
+    A container for transporting a list of beliefs between agents.
-
-    :ivar create: Beliefs to create.
-    :ivar delete: Beliefs to delete.
-    :ivar replace: Beliefs to replace. Deletes all beliefs with the same name, replacing them with
-        one new belief.
     """
 
-    create: list[Belief] = []
+    beliefs: list[Belief]
-    delete: list[Belief] = []
-    replace: list[Belief] = []
-
-    def has_values(self) -> bool:
-        return len(self.create) > 0 or len(self.delete) > 0 or len(self.replace) > 0

src/control_backend/schemas/chat_history.py

@@ -1,10 +0,0 @@
-from pydantic import BaseModel
-
-
-class ChatMessage(BaseModel):
-    role: str
-    content: str
-
-
-class ChatHistory(BaseModel):
-    messages: list[ChatMessage]

src/control_backend/schemas/events.py

@@ -1,6 +0,0 @@
-from pydantic import BaseModel
-
-
-class ButtonPressedEvent(BaseModel):
-    type: str
-    context: str

src/control_backend/schemas/ri_message.py

@@ -14,7 +14,6 @@ class RIEndpoint(str, Enum):
     GESTURE_TAG = "actuate/gesture/tag"
     PING = "ping"
     NEGOTIATE_PORTS = "negotiate/ports"
-    PAUSE = "pause"
 
 
 class RIMessage(BaseModel):
@@ -39,7 +38,6 @@ class SpeechCommand(RIMessage):
 
     endpoint: RIEndpoint = RIEndpoint(RIEndpoint.SPEECH)
     data: str
-    is_priority: bool = False
 
 
 class GestureCommand(RIMessage):
@@ -54,7 +52,6 @@ class GestureCommand(RIMessage):
         RIEndpoint.GESTURE_SINGLE, RIEndpoint.GESTURE_TAG
     ]
     data: str
-    is_priority: bool = False
 
     @model_validator(mode="after")
     def check_endpoint(self):
@@ -65,14 +62,3 @@ class GestureCommand(RIMessage):
         if self.endpoint not in allowed:
             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.
-
-    :ivar endpoint: Fixed to ``RIEndpoint.PAUSE``.
-    :ivar data: A boolean indicating whether to pause (True) or unpause (False).
-    """
-
-    endpoint: RIEndpoint = RIEndpoint(RIEndpoint.PAUSE)
-    data: bool

test/unit/agents/actuation/test_robot_speech_agent.py

@@ -64,7 +64,7 @@ async def test_handle_message_sends_command():
     agent = mock_speech_agent()
     agent.pubsocket = pubsocket
 
-    payload = {"endpoint": "actuate/speech", "data": "hello", "is_priority": False}
+    payload = {"endpoint": "actuate/speech", "data": "hello"}
     msg = InternalMessage(to="robot", sender="tester", body=json.dumps(payload))
 
     await agent.handle_message(msg)
@@ -75,7 +75,7 @@ async def test_handle_message_sends_command():
 @pytest.mark.asyncio
 async def test_zmq_command_loop_valid_payload(zmq_context):
     """UI command is read from SUB and published."""
-    command = {"endpoint": "actuate/speech", "data": "hello", "is_priority": False}
+    command = {"endpoint": "actuate/speech", "data": "hello"}
     fake_socket = AsyncMock()
 
     async def recv_once():

test/unit/agents/bdi/test_bdi_core_agent.py

@@ -51,7 +51,7 @@ async def test_handle_belief_collector_message(agent, mock_settings):
     msg = InternalMessage(
         to="bdi_agent",
         sender=mock_settings.agent_settings.bdi_belief_collector_name,
-        body=BeliefMessage(create=beliefs).model_dump_json(),
+        body=BeliefMessage(beliefs=beliefs).model_dump_json(),
         thread="beliefs",
     )
 
@@ -64,26 +64,6 @@ async def test_handle_belief_collector_message(agent, mock_settings):
     assert args[2] == agentspeak.Literal("user_said", (agentspeak.Literal("Hello"),))
 
 
-@pytest.mark.asyncio
-async def test_handle_delete_belief_message(agent, mock_settings):
-    """Test that incoming beliefs to be deleted are removed from the BDI agent"""
-    beliefs = [Belief(name="user_said", arguments=["Hello"])]
-
-    msg = InternalMessage(
-        to="bdi_agent",
-        sender=mock_settings.agent_settings.bdi_belief_collector_name,
-        body=BeliefMessage(delete=beliefs).model_dump_json(),
-        thread="beliefs",
-    )
-    await agent.handle_message(msg)
-
-    # Expect bdi_agent.call to be triggered to remove belief
-    args = agent.bdi_agent.call.call_args.args
-    assert args[0] == agentspeak.Trigger.removal
-    assert args[1] == agentspeak.GoalType.belief
-    assert args[2] == agentspeak.Literal("user_said", (agentspeak.Literal("Hello"),))
-
-
 @pytest.mark.asyncio
 async def test_incorrect_belief_collector_message(agent, mock_settings):
     """Test that incorrect message format triggers an exception."""
@@ -148,8 +128,7 @@ def test_add_belief_sets_event(agent):
     agent._wake_bdi_loop = MagicMock()
 
     belief = Belief(name="test_belief", arguments=["a", "b"])
-    belief_changes = BeliefMessage(replace=[belief])
+    agent._apply_beliefs([belief])
-    agent._apply_belief_changes(belief_changes)
 
     assert agent.bdi_agent.call.called
     agent._wake_bdi_loop.set.assert_called()
@@ -158,7 +137,7 @@ def test_add_belief_sets_event(agent):
 def test_apply_beliefs_empty_returns(agent):
     """Line: if not beliefs: return"""
     agent._wake_bdi_loop = MagicMock()
-    agent._apply_belief_changes(BeliefMessage())
+    agent._apply_beliefs([])
     agent.bdi_agent.call.assert_not_called()
     agent._wake_bdi_loop.set.assert_not_called()
 
@@ -241,9 +220,8 @@ def test_replace_belief_calls_remove_all(agent):
     agent._remove_all_with_name = MagicMock()
     agent._wake_bdi_loop = MagicMock()
 
-    belief = Belief(name="user_said", arguments=["Hello"])
+    belief = Belief(name="user_said", arguments=["Hello"], replace=True)
-    belief_changes = BeliefMessage(replace=[belief])
+    agent._apply_beliefs([belief])
-    agent._apply_belief_changes(belief_changes)
 
     agent._remove_all_with_name.assert_called_with("user_said")
 

test/unit/agents/bdi/test_bdi_program_manager.py

@@ -1,6 +1,6 @@
 import asyncio
+import json
 import sys
-import uuid
 from unittest.mock import AsyncMock
 
 import pytest
@@ -8,52 +8,38 @@ import pytest
 from control_backend.agents.bdi.bdi_program_manager import BDIProgramManager
 from control_backend.core.agent_system import InternalMessage
 from control_backend.schemas.belief_message import BeliefMessage
-from control_backend.schemas.program import BasicNorm, Goal, Phase, Plan, Program
+from control_backend.schemas.program import Program
 
 # Fix Windows Proactor loop for zmq
 if sys.platform.startswith("win"):
     asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
 
 
-def make_valid_program_json(norm="N1", goal="G1") -> str:
+def make_valid_program_json(norm="N1", goal="G1"):
-    return Program(
+    return json.dumps(
-        phases=[
+        {
-            Phase(
+            "phases": [
-                id=uuid.uuid4(),
+                {
-                name="Basic Phase",
+                    "id": "phase1",
-                norms=[
+                    "label": "Phase 1",
-                    BasicNorm(
+                    "triggers": [],
-                        id=uuid.uuid4(),
+                    "norms": [{"id": "n1", "label": "Norm 1", "norm": norm}],
-                        name=norm,
+                    "goals": [
-                        norm=norm,
+                        {"id": "g1", "label": "Goal 1", "description": goal, "achieved": False}
-                    ),
+                    ],
-                ],
+                }
-                goals=[
+            ]
-                    Goal(
+        }
-                        id=uuid.uuid4(),
+    )
-                        name=goal,
-                        plan=Plan(
-                            id=uuid.uuid4(),
-                            name="Goal Plan",
-                            steps=[],
-                        ),
-                        can_fail=False,
-                    ),
-                ],
-                triggers=[],
-            ),
-        ],
-    ).model_dump_json()
 
 
-@pytest.mark.skip(reason="Functionality being rebuilt.")
 @pytest.mark.asyncio
 async def test_send_to_bdi():
     manager = BDIProgramManager(name="program_manager_test")
     manager.send = AsyncMock()
 
     program = Program.model_validate_json(make_valid_program_json())
-    await manager._send_to_bdi(program)
+    await manager._create_agentspeak_and_send_to_bdi(program)
 
     assert manager.send.await_count == 1
     msg: InternalMessage = manager.send.await_args[0][0]
@@ -76,7 +62,7 @@ async def test_receive_programs_valid_and_invalid():
 
     manager = BDIProgramManager(name="program_manager_test")
     manager.sub_socket = sub
-    manager._send_to_bdi = AsyncMock()
+    manager._create_agentspeak_and_send_to_bdi = AsyncMock()
 
     try:
         # Will give StopAsyncIteration when the predefined `sub.recv_multipart` side-effects run out
@@ -85,7 +71,7 @@ async def test_receive_programs_valid_and_invalid():
         pass
 
     # Only valid Program should have triggered _send_to_bdi
-    assert manager._send_to_bdi.await_count == 1
+    assert manager._create_agentspeak_and_send_to_bdi.await_count == 1
-    forwarded: Program = manager._send_to_bdi.await_args[0][0]
+    forwarded: Program = manager._create_agentspeak_and_send_to_bdi.await_args[0][0]
-    assert forwarded.phases[0].norms[0].name == "N1"
+    assert forwarded.phases[0].norms[0].norm == "N1"
-    assert forwarded.phases[0].goals[0].name == "G1"
+    assert forwarded.phases[0].goals[0].description == "G1"

test/unit/agents/bdi/test_belief_collector.py

@@ -86,7 +86,7 @@ async def test_send_beliefs_to_bdi(agent):
     sent: InternalMessage = agent.send.call_args.args[0]
     assert sent.to == settings.agent_settings.bdi_core_name
     assert sent.thread == "beliefs"
-    assert json.loads(sent.body)["create"] == [belief.model_dump() for belief in beliefs]
+    assert json.loads(sent.body)["beliefs"] == [belief.model_dump() for belief in beliefs]
 
 
 @pytest.mark.asyncio

test/unit/agents/bdi/test_text_belief_extractor.py

@@ -1,346 +0,0 @@
-import json
-import uuid
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import httpx
-import pytest
-
-from control_backend.agents.bdi import TextBeliefExtractorAgent
-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.program import (
-    ConditionalNorm,
-    LLMAction,
-    Phase,
-    Plan,
-    Program,
-    SemanticBelief,
-    Trigger,
-)
-
-
-@pytest.fixture
-def agent():
-    agent = TextBeliefExtractorAgent("text_belief_agent")
-    agent.send = AsyncMock()
-    agent._query_llm = AsyncMock()
-    return agent
-
-
-@pytest.fixture
-def sample_program():
-    return Program(
-        phases=[
-            Phase(
-                name="Some phase",
-                id=uuid.uuid4(),
-                norms=[
-                    ConditionalNorm(
-                        name="Some norm",
-                        id=uuid.uuid4(),
-                        norm="Use nautical terms.",
-                        critical=False,
-                        condition=SemanticBelief(
-                            name="is_pirate",
-                            id=uuid.uuid4(),
-                            description="The user is a pirate. Perhaps because they say "
-                            "they are, or because they speak like a pirate "
-                            'with terms like "arr".',
-                        ),
-                    ),
-                ],
-                goals=[],
-                triggers=[
-                    Trigger(
-                        name="Some trigger",
-                        id=uuid.uuid4(),
-                        condition=SemanticBelief(
-                            name="no_more_booze",
-                            id=uuid.uuid4(),
-                            description="There is no more alcohol.",
-                        ),
-                        plan=Plan(
-                            name="Some plan",
-                            id=uuid.uuid4(),
-                            steps=[
-                                LLMAction(
-                                    name="Some action",
-                                    id=uuid.uuid4(),
-                                    goal="Suggest eating chocolate instead.",
-                                ),
-                            ],
-                        ),
-                    ),
-                ],
-            ),
-        ],
-    )
-
-
-def make_msg(sender: str, body: str, thread: str | None = None) -> InternalMessage:
-    return InternalMessage(to="unused", sender=sender, body=body, thread=thread)
-
-
-@pytest.mark.asyncio
-async def test_handle_message_ignores_other_agents(agent):
-    msg = make_msg("unknown", "some data", None)
-
-    await agent.handle_message(msg)
-
-    agent.send.assert_not_called()  # noqa  # `agent.send` has no such property, but we mock it.
-
-
-@pytest.mark.asyncio
-async def test_handle_message_from_transcriber(agent, mock_settings):
-    transcription = "hello world"
-    msg = make_msg(mock_settings.agent_settings.transcription_name, transcription, None)
-
-    await agent.handle_message(msg)
-
-    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]}, "type": "belief_extraction_text"}
-
-
-@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
-async def test_query_llm():
-    mock_response = MagicMock()
-    mock_response.json.return_value = {
-        "choices": [
-            {
-                "message": {
-                    "content": "null",
-                }
-            }
-        ]
-    }
-    mock_client = AsyncMock()
-    mock_client.post.return_value = mock_response
-    mock_async_client = MagicMock()
-    mock_async_client.__aenter__.return_value = mock_client
-    mock_async_client.__aexit__.return_value = None
-
-    with patch(
-        "control_backend.agents.bdi.text_belief_extractor_agent.httpx.AsyncClient",
-        return_value=mock_async_client,
-    ):
-        agent = TextBeliefExtractorAgent("text_belief_agent")
-
-        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(agent):
-    agent._query_llm.return_value = None
-    res = await agent._retry_query_llm("hello world", {"type": "null"})
-
-    agent._query_llm.assert_called_once()
-    assert res is None
-
-
-@pytest.mark.asyncio
-async def test_retry_query_llm_success_after_failure(agent):
-    agent._query_llm.side_effect = [KeyError(), "real value"]
-    res = await agent._retry_query_llm("hello world", {"type": "string"})
-
-    assert agent._query_llm.call_count == 2
-    assert res == "real value"
-
-
-@pytest.mark.asyncio
-async def test_retry_query_llm_failures(agent):
-    agent._query_llm.side_effect = [KeyError(), KeyError(), KeyError(), "real value"]
-    res = await agent._retry_query_llm("hello world", {"type": "string"})
-
-    assert agent._query_llm.call_count == 3
-    assert res is None
-
-
-@pytest.mark.asyncio
-async def test_retry_query_llm_fail_immediately(agent):
-    agent._query_llm.side_effect = [KeyError(), "real value"]
-    res = await agent._retry_query_llm("hello world", {"type": "string"}, tries=1)
-
-    assert agent._query_llm.call_count == 1
-    assert res is None
-
-
-@pytest.mark.asyncio
-async def test_extracting_beliefs_from_program(agent, sample_program):
-    assert len(agent.available_beliefs) == 0
-    await agent.handle_message(
-        InternalMessage(
-            to=settings.agent_settings.text_belief_extractor_name,
-            sender=settings.agent_settings.bdi_program_manager_name,
-            body=sample_program.model_dump_json(),
-        ),
-    )
-    assert len(agent.available_beliefs) == 2
-
-
-@pytest.mark.asyncio
-async def test_handle_invalid_program(agent, sample_program):
-    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    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"}),
-        ),
-    )
-
-    assert len(agent.available_beliefs) == 2
-
-
-@pytest.mark.asyncio
-async def test_handle_robot_response(agent):
-    initial_length = len(agent.conversation.messages)
-    response = "Hi, I'm Pepper. What's your name?"
-
-    await agent.handle_message(
-        InternalMessage(
-            to=settings.agent_settings.text_belief_extractor_name,
-            sender=settings.agent_settings.llm_name,
-            body=response,
-        ),
-    )
-
-    assert len(agent.conversation.messages) == initial_length + 1
-    assert agent.conversation.messages[-1].role == "assistant"
-    assert agent.conversation.messages[-1].content == response
-
-
-@pytest.mark.asyncio
-async def test_simulated_real_turn_with_beliefs(agent, sample_program):
-    """Test sending user message to extract beliefs from."""
-    agent.available_beliefs.append(sample_program.phases[0].norms[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
-    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": True}
-    assert len(agent.conversation.messages) == 0
-    await agent.handle_message(
-        InternalMessage(
-            to=settings.agent_settings.text_belief_extractor_name,
-            sender=settings.agent_settings.transcription_name,
-            body="We're all out of schnaps.",
-        ),
-    )
-    assert len(agent.conversation.messages) == 1
-
-    # There should be a belief set and sent to the BDI core, as well as the user_said belief
-    assert agent.send.call_count == 2
-
-    # First should be the beliefs message
-    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, sample_program):
-    """Test a user message to extract beliefs from, but no beliefs are formed."""
-    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-
-    # Send a user message with no new beliefs
-    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,
-            sender=settings.agent_settings.transcription_name,
-            body="Hello there!",
-        ),
-    )
-
-    # Only the user_said belief should've been sent
-    agent.send.assert_called_once()
-
-
-@pytest.mark.asyncio
-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.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    agent.beliefs["is_pirate"] = True
-
-    # Send a user message with the belief the user is a pirate, still
-    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,
-            sender=settings.agent_settings.transcription_name,
-            body="Arr, nice to meet you, matey.",
-        ),
-    )
-
-    # Only the user_said belief should've been sent, as no beliefs have changed
-    agent.send.assert_called_once()
-
-
-@pytest.mark.asyncio
-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.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    agent.beliefs["no_more_booze"] = True
-
-    # Send a user message with the belief the user is a pirate, still
-    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,
-            sender=settings.agent_settings.transcription_name,
-            body="I found an untouched barrel of wine!",
-        ),
-    )
-
-    # Both user_said and belief change should've been sent
-    assert agent.send.call_count == 2
-
-    # Agent's current beliefs should've changed
-    assert not agent.beliefs["no_more_booze"]
-
-
-@pytest.mark.asyncio
-async def test_llm_failure_handling(agent, sample_program):
-    """
-    Check that the agent handles failures gracefully without crashing.
-    """
-    agent._query_llm.side_effect = httpx.HTTPError("")
-    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-
-    belief_changes = await agent._infer_turn()
-
-    assert len(belief_changes) == 0

test/unit/agents/bdi/test_text_extractor.py

@@ -0,0 +1,65 @@
+import json
+from unittest.mock import AsyncMock
+
+import pytest
+
+from control_backend.agents.bdi import (
+    TextBeliefExtractorAgent,
+)
+from control_backend.core.agent_system import InternalMessage
+
+
+@pytest.fixture
+def agent():
+    agent = TextBeliefExtractorAgent("text_belief_agent")
+    agent.send = AsyncMock()
+    return agent
+
+
+def make_msg(sender: str, body: str, thread: str | None = None) -> InternalMessage:
+    return InternalMessage(to="unused", sender=sender, body=body, thread=thread)
+
+
+@pytest.mark.asyncio
+async def test_handle_message_ignores_other_agents(agent):
+    msg = make_msg("unknown", "some data", None)
+
+    await agent.handle_message(msg)
+
+    agent.send.assert_not_called()  # noqa  # `agent.send` has no such property, but we mock it.
+
+
+@pytest.mark.asyncio
+async def test_handle_message_from_transcriber(agent, mock_settings):
+    transcription = "hello world"
+    msg = make_msg(mock_settings.agent_settings.transcription_name, transcription, None)
+
+    await agent.handle_message(msg)
+
+    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]}, "type": "belief_extraction_text"}
+
+
+@pytest.mark.asyncio
+async def test_process_transcription_demo(agent, mock_settings):
+    transcription = "this is a test"
+
+    await agent._process_transcription_demo(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
+async def test_setup_initializes_beliefs(agent):
+    """Covers the setup method and ensures beliefs are initialized."""
+    await agent.setup()
+    assert agent.beliefs == {"mood": ["X"], "car": ["Y"]}

test/unit/agents/communication/test_ri_communication_agent.py

@@ -67,7 +67,6 @@ async def test_setup_success_connects_and_starts_robot(zmq_context):
         address="tcp://localhost:5556",
         bind=False,
         gesture_data=[],
-        single_gesture_data=[],
     )
     agent.add_behavior.assert_called_once()
 

test/unit/agents/llm/test_llm_agent.py

@@ -66,7 +66,7 @@ async def test_llm_processing_success(mock_httpx_client, mock_settings):
     # "Hello world." constitutes one sentence/chunk based on punctuation split
     # The agent should call send once with the full sentence
     assert agent.send.called
-    args = agent.send.call_args_list[0][0][0]
+    args = agent.send.call_args[0][0]
     assert args.to == mock_settings.agent_settings.bdi_core_name
     assert "Hello world." in args.body
 
@@ -197,9 +197,6 @@ async def test_query_llm_yields_final_tail_chunk(mock_settings):
     agent = LLMAgent("llm_agent")
     agent.send = AsyncMock()
 
-    agent.logger = MagicMock()
-    agent.logger.llm = MagicMock()
-
     # Patch _stream_query_llm to yield tokens that do NOT end with punctuation
     async def fake_stream(messages):
         yield "Hello"

test/unit/agents/user_interrupt/test_user_interrupt.py

@@ -1,146 +0,0 @@
-import asyncio
-import json
-from unittest.mock import AsyncMock, MagicMock
-
-import pytest
-
-from control_backend.agents.user_interrupt.user_interrupt_agent import UserInterruptAgent
-from control_backend.core.agent_system import InternalMessage
-from control_backend.core.config import settings
-from control_backend.schemas.ri_message import RIEndpoint
-
-
-@pytest.fixture
-def agent():
-    agent = UserInterruptAgent(name="user_interrupt_agent")
-    agent.send = AsyncMock()
-    agent.logger = MagicMock()
-    agent.sub_socket = AsyncMock()
-    return agent
-
-
-@pytest.mark.asyncio
-async def test_send_to_speech_agent(agent):
-    """Verify speech command format."""
-    await agent._send_to_speech_agent("Hello World")
-
-    agent.send.assert_awaited_once()
-    sent_msg: InternalMessage = agent.send.call_args.args[0]
-
-    assert sent_msg.to == settings.agent_settings.robot_speech_name
-    body = json.loads(sent_msg.body)
-    assert body["data"] == "Hello World"
-    assert body["is_priority"] is True
-
-
-@pytest.mark.asyncio
-async def test_send_to_gesture_agent(agent):
-    """Verify gesture command format."""
-    await agent._send_to_gesture_agent("wave_hand")
-
-    agent.send.assert_awaited_once()
-    sent_msg: InternalMessage = agent.send.call_args.args[0]
-
-    assert sent_msg.to == settings.agent_settings.robot_gesture_name
-    body = json.loads(sent_msg.body)
-    assert body["data"] == "wave_hand"
-    assert body["is_priority"] is True
-    assert body["endpoint"] == RIEndpoint.GESTURE_SINGLE.value
-
-
-@pytest.mark.asyncio
-async def test_send_to_program_manager(agent):
-    """Verify belief update format."""
-    context_str = "2"
-
-    await agent._send_to_program_manager(context_str)
-
-    agent.send.assert_awaited_once()
-    sent_msg: InternalMessage = agent.send.call_args.args[0]
-
-    assert sent_msg.to == settings.agent_settings.bdi_program_manager_name
-    assert sent_msg.thread == "belief_override_id"
-
-    body = json.loads(sent_msg.body)
-
-    assert body["belief"] == context_str
-
-
-@pytest.mark.asyncio
-async def test_receive_loop_routing_success(agent):
-    """
-    Test that the loop correctly:
-    1. Receives 'button_pressed' topic from ZMQ
-    2. Parses the JSON payload to find 'type' and 'context'
-    3. Calls the correct handler method based on 'type'
-    """
-    # Prepare JSON payloads as bytes
-    payload_speech = json.dumps({"type": "speech", "context": "Hello Speech"}).encode()
-    payload_gesture = json.dumps({"type": "gesture", "context": "Hello Gesture"}).encode()
-    payload_override = json.dumps({"type": "override", "context": "Hello Override"}).encode()
-
-    agent.sub_socket.recv_multipart.side_effect = [
-        (b"button_pressed", payload_speech),
-        (b"button_pressed", payload_gesture),
-        (b"button_pressed", payload_override),
-        asyncio.CancelledError,  # Stop the infinite loop
-    ]
-
-    agent._send_to_speech_agent = AsyncMock()
-    agent._send_to_gesture_agent = AsyncMock()
-    agent._send_to_program_manager = AsyncMock()
-
-    try:
-        await agent._receive_button_event()
-    except asyncio.CancelledError:
-        pass
-
-    await asyncio.sleep(0)
-
-    # Speech
-    agent._send_to_speech_agent.assert_awaited_once_with("Hello Speech")
-
-    # Gesture
-    agent._send_to_gesture_agent.assert_awaited_once_with("Hello Gesture")
-
-    # Override
-    agent._send_to_program_manager.assert_awaited_once_with("Hello Override")
-
-    assert agent._send_to_speech_agent.await_count == 1
-    assert agent._send_to_gesture_agent.await_count == 1
-    assert agent._send_to_program_manager.await_count == 1
-
-
-@pytest.mark.asyncio
-async def test_receive_loop_unknown_type(agent):
-    """Test that unknown 'type' values in the JSON log a warning and do not crash."""
-
-    # Prepare a payload with an unknown type
-    payload_unknown = json.dumps({"type": "unknown_thing", "context": "some_data"}).encode()
-
-    agent.sub_socket.recv_multipart.side_effect = [
-        (b"button_pressed", payload_unknown),
-        asyncio.CancelledError,
-    ]
-
-    agent._send_to_speech_agent = AsyncMock()
-    agent._send_to_gesture_agent = AsyncMock()
-    agent._send_to_belief_collector = AsyncMock()
-
-    try:
-        await agent._receive_button_event()
-    except asyncio.CancelledError:
-        pass
-
-    await asyncio.sleep(0)
-
-    # Ensure no handlers were called
-    agent._send_to_speech_agent.assert_not_called()
-    agent._send_to_gesture_agent.assert_not_called()
-    agent._send_to_belief_collector.assert_not_called()
-
-    agent.logger.warning.assert_called_with(
-        "Received button press with unknown type '%s' (context: '%s').",
-        "unknown_thing",
-        "some_data",
-    )

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

@@ -1,5 +1,4 @@
 import json
-import uuid
 from unittest.mock import AsyncMock
 
 import pytest
@@ -7,7 +6,7 @@ from fastapi import FastAPI
 from fastapi.testclient import TestClient
 
 from control_backend.api.v1.endpoints import program
-from control_backend.schemas.program import BasicNorm, Goal, Phase, Plan, Program
+from control_backend.schemas.program import Program
 
 
 @pytest.fixture
@@ -26,37 +25,29 @@ def client(app):
 
 def make_valid_program_dict():
     """Helper to create a valid Program JSON structure."""
-    # Converting to JSON using Pydantic because it knows how to convert a UUID object
+    return {
-    program_json_str = Program(
+        "phases": [
-        phases=[
+            {
-            Phase(
+                "id": "phase1",
-                id=uuid.uuid4(),
+                "label": "basephase",
-                name="Basic Phase",
+                "norms": [{"id": "n1", "label": "norm", "norm": "be nice"}],
-                norms=[
+                "goals": [
-                    BasicNorm(
+                    {"id": "g1", "label": "goal", "description": "test goal", "achieved": False}
-                        id=uuid.uuid4(),
-                        name="Some norm",
-                        norm="Do normal.",
-                    ),
                 ],
-                goals=[
+                "triggers": [
-                    Goal(
+                    {
-                        id=uuid.uuid4(),
+                        "id": "t1",
-                        name="Some goal",
+                        "label": "trigger",
-                        plan=Plan(
+                        "type": "keywords",
-                            id=uuid.uuid4(),
+                        "keywords": [
-                            name="Goal Plan",
+                            {"id": "kw1", "keyword": "keyword1"},
-                            steps=[],
+                            {"id": "kw2", "keyword": "keyword2"},
-                        ),
+                        ],
-                        can_fail=False,
+                    },
-                    ),
                 ],
-                triggers=[],
+            }
-            ),
+        ]
-        ],
+    }
-    ).model_dump_json()
-    # Converting back to a dict because that's what's expected
-    return json.loads(program_json_str)
 
 
 def test_receive_program_success(client):
@@ -80,8 +71,7 @@ def test_receive_program_success(client):
     sent_bytes = args[0][1]
     sent_obj = json.loads(sent_bytes.decode())
 
-    # Converting to JSON using Pydantic because it knows how to handle UUIDs
+    expected_obj = Program.model_validate(program_dict).model_dump()
-    expected_obj = json.loads(Program.model_validate(program_dict).model_dump_json())
     assert sent_obj == expected_obj
 
 

test/unit/schemas/test_ui_program_message.py

@@ -1,65 +1,49 @@
-import uuid
-
 import pytest
 from pydantic import ValidationError
 
 from control_backend.schemas.program import (
-    BasicNorm,
-    ConditionalNorm,
     Goal,
-    InferredBelief,
+    KeywordTrigger,
-    KeywordBelief,
+    Norm,
-    LogicalOperator,
     Phase,
-    Plan,
     Program,
-    SemanticBelief,
+    TriggerKeyword,
-    Trigger,
 )
 
 
-def base_norm() -> BasicNorm:
+def base_norm() -> Norm:
-    return BasicNorm(
+    return Norm(
-        id=uuid.uuid4(),
+        id="norm1",
-        name="testNormName",
+        label="testNorm",
         norm="testNormNorm",
-        critical=False,
     )
 
 
 def base_goal() -> Goal:
     return Goal(
-        id=uuid.uuid4(),
+        id="goal1",
-        name="testGoalName",
+        label="testGoal",
-        plan=Plan(
+        description="testGoalDescription",
-            id=uuid.uuid4(),
+        achieved=False,
-            name="testGoalPlanName",
-            steps=[],
-        ),
-        can_fail=False,
     )
 
 
-def base_trigger() -> Trigger:
+def base_trigger() -> KeywordTrigger:
-    return Trigger(
+    return KeywordTrigger(
-        id=uuid.uuid4(),
+        id="trigger1",
-        name="testTriggerName",
+        label="testTrigger",
-        condition=KeywordBelief(
+        type="keywords",
-            id=uuid.uuid4(),
+        keywords=[
-            name="testTriggerKeywordBeliefTriggerName",
+            TriggerKeyword(id="keyword1", keyword="testKeyword1"),
-            keyword="Keyword",
+            TriggerKeyword(id="keyword1", keyword="testKeyword2"),
-        ),
+        ],
-        plan=Plan(
-            id=uuid.uuid4(),
-            name="testTriggerPlanName",
-            steps=[],
-        ),
     )
 
 
 def base_phase() -> Phase:
     return Phase(
-        id=uuid.uuid4(),
+        id="phase1",
+        label="basephase",
         norms=[base_norm()],
         goals=[base_goal()],
         triggers=[base_trigger()],
@@ -74,7 +58,7 @@ def invalid_program() -> dict:
     # wrong types inside phases list (not Phase objects)
     return {
         "phases": [
-            {"id": uuid.uuid4()},  # incomplete
+            {"id": "phase1"},  # incomplete
             {"not_a_phase": True},
         ]
     }
@@ -93,112 +77,11 @@ def test_valid_deepprogram():
     # validate nested components directly
     phase = validated.phases[0]
     assert isinstance(phase.goals[0], Goal)
-    assert isinstance(phase.triggers[0], Trigger)
+    assert isinstance(phase.triggers[0], KeywordTrigger)
-    assert isinstance(phase.norms[0], BasicNorm)
+    assert isinstance(phase.norms[0], Norm)
 
 
 def test_invalid_program():
     bad = invalid_program()
     with pytest.raises(ValidationError):
         Program.model_validate(bad)
-
-
-def test_conditional_norm_parsing():
-    """
-    Check that pydantic is able to preserve the type of the norm, that it doesn't lose its
-    "condition" field when serializing and deserializing.
-    """
-    norm = ConditionalNorm(
-        name="testNormName",
-        id=uuid.uuid4(),
-        norm="testNormNorm",
-        critical=False,
-        condition=KeywordBelief(
-            name="testKeywordBelief",
-            id=uuid.uuid4(),
-            keyword="testKeywordBelief",
-        ),
-    )
-    program = Program(
-        phases=[
-            Phase(
-                name="Some phase",
-                id=uuid.uuid4(),
-                norms=[norm],
-                goals=[],
-                triggers=[],
-            ),
-        ],
-    )
-
-    parsed_program = Program.model_validate_json(program.model_dump_json())
-    parsed_norm = parsed_program.phases[0].norms[0]
-
-    assert hasattr(parsed_norm, "condition")
-    assert isinstance(parsed_norm, ConditionalNorm)
-
-
-def test_belief_type_parsing():
-    """
-    Check that pydantic is able to discern between the different types of beliefs when serializing
-    and deserializing.
-    """
-    keyword_belief = KeywordBelief(
-        name="testKeywordBelief",
-        id=uuid.uuid4(),
-        keyword="something",
-    )
-    semantic_belief = SemanticBelief(
-        name="testSemanticBelief",
-        id=uuid.uuid4(),
-        description="something",
-    )
-    inferred_belief = InferredBelief(
-        name="testInferredBelief",
-        id=uuid.uuid4(),
-        operator=LogicalOperator.OR,
-        left=keyword_belief,
-        right=semantic_belief,
-    )
-
-    program = Program(
-        phases=[
-            Phase(
-                name="Some phase",
-                id=uuid.uuid4(),
-                norms=[],
-                goals=[],
-                triggers=[
-                    Trigger(
-                        name="testTriggerKeywordTrigger",
-                        id=uuid.uuid4(),
-                        condition=keyword_belief,
-                        plan=Plan(name="testTriggerPlanName", id=uuid.uuid4(), steps=[]),
-                    ),
-                    Trigger(
-                        name="testTriggerSemanticTrigger",
-                        id=uuid.uuid4(),
-                        condition=semantic_belief,
-                        plan=Plan(name="testTriggerPlanName", id=uuid.uuid4(), steps=[]),
-                    ),
-                    Trigger(
-                        name="testTriggerInferredTrigger",
-                        id=uuid.uuid4(),
-                        condition=inferred_belief,
-                        plan=Plan(name="testTriggerPlanName", id=uuid.uuid4(), steps=[]),
-                    ),
-                ],
-            ),
-        ],
-    )
-
-    parsed_program = Program.model_validate_json(program.model_dump_json())
-
-    parsed_keyword_belief = parsed_program.phases[0].triggers[0].condition
-    assert isinstance(parsed_keyword_belief, KeywordBelief)
-
-    parsed_semantic_belief = parsed_program.phases[0].triggers[1].condition
-    assert isinstance(parsed_semantic_belief, SemanticBelief)
-
-    parsed_inferred_belief = parsed_program.phases[0].triggers[2].condition
-    assert isinstance(parsed_inferred_belief, InferredBelief)