feat: add reset functionality to semantic belief extractor

ref: N25B-432

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 +1,403 @@
+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(self.get_semantic_belief_slug(sb))
+
+    @staticmethod
+    def get_semantic_belief_slug(sb: SemanticBelief) -> str:
+        # If you need a method like this for other types, make a public slugify singledispatch for
+        #  all types.
+        return f"semantic_{AgentSpeakGenerator._slugify_str(sb.name)}"
+
+    @_astify.register
+    def _(self, ib: InferredBelief) -> AstExpression:
+        return AstBinaryOp(
+            self._astify(ib.left),
+            BinaryOperatorType.AND if ib.operator == LogicalOperator.AND else BinaryOperatorType.OR,
+            self._astify(ib.right),
+        )
+
+    @_astify.register
+    def _(self, norm: Norm) -> AstExpression:
+        functor = "critical_norm" if norm.critical else "norm"
+        return AstLiteral(functor, [AstString(norm.norm)])
+
+    @_astify.register
+    def _(self, phase: Phase) -> AstExpression:
+        return AstLiteral("phase", [AstString(str(phase.id))])
+
+    @_astify.register
+    def _(self, goal: Goal, achieved: bool = False) -> AstExpression:
+        return AstLiteral(f"{'achieved_' if achieved else ''}{self._slugify_str(goal.name)}")
+
+    @_astify.register
+    def _(self, trigger: Trigger) -> AstExpression:
+        return AstLiteral(self.slugify(trigger))
+
+    @_astify.register
+    def _(self, sa: SpeechAction) -> AstExpression:
+        return AstLiteral("say", [AstString(sa.text)])
+
+    @_astify.register
+    def _(self, ga: GestureAction) -> AstExpression:
+        gesture = ga.gesture
+        return AstLiteral("gesture", [AstString(gesture.type), AstString(gesture.name)])
+
+    @_astify.register
+    def _(self, la: LLMAction) -> AstExpression:
+        return AstLiteral("reply_with_goal", [AstString(la.goal)])
+
+    @singledispatchmethod
+    @staticmethod
+    def slugify(element: ProgramElement) -> str:
+        raise NotImplementedError(f"Cannot convert element {element} to a slug.")
+
+    @slugify.register
+    @staticmethod
+    def _(sb: SemanticBelief) -> str:
+        return f"semantic_{AgentSpeakGenerator._slugify_str(sb.name)}"
+
+    @slugify.register
+    @staticmethod
+    def _(g: Goal) -> str:
+        return AgentSpeakGenerator._slugify_str(g.name)
+
+    @slugify.register
+    @staticmethod
+    def _(t: Trigger):
+        return f"trigger_{AgentSpeakGenerator._slugify_str(t.name)}"
+
+    @staticmethod
+    def _slugify_str(text: str) -> str:
+        return slugify(text, separator="_", stopwords=["a", "an", "the", "we", "you", "I"])

src/control_backend/agents/bdi/bdi_core_agent.py

@@ -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.
@@ -130,6 +134,13 @@ class BDICoreAgent(BaseAgent):
                 self.logger.exception("Error processing belief.")
             return
 
+        # New agentspeak file
+        if msg.thread == "new_program":
+            if self._bdi_loop_task:
+                self._bdi_loop_task.cancel()
+            await self._load_asl(msg.body)
+            self.add_behavior(self._bdi_loop())
+
         # The message was not a belief, handle special cases based on sender
         match msg.sender:
             case settings.agent_settings.llm_name:
@@ -194,12 +205,15 @@ class BDICoreAgent(BaseAgent):
 
         self.logger.debug(f"Added belief {self.format_belief_string(name, args)}")
 
-    def _remove_belief(self, name: str, args: Iterable[str]):
+    def _remove_belief(self, name: str, args: Iterable[str] | None):
         """
         Removes a specific belief (with arguments), if it exists.
         """
-        new_args = (agentspeak.Literal(arg) for arg in args)
-        term = agentspeak.Literal(name, new_args)
+        if args is None:
+            term = agentspeak.Literal(name)
+        else:
+            new_args = (agentspeak.Literal(arg) for arg in args)
+            term = agentspeak.Literal(name, new_args)
 
         result = self.bdi_agent.call(
             agentspeak.Trigger.removal,
@@ -246,20 +260,18 @@ 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 +290,7 @@ 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 +302,14 @@ class BDICoreAgent(BaseAgent):
 
             self.logger.debug('"say" action called with text=%s', message_text)
 
-            # speech_command = SpeechCommand(data=message_text)
-            # speech_message = InternalMessage(
-            #     to=settings.agent_settings.robot_speech_name,
-            #     sender=settings.agent_settings.bdi_core_name,
-            #     body=speech_command.model_dump_json(),
-            # )
-            # asyncio.create_task(agent.send(speech_message))
+            speech_command = SpeechCommand(data=message_text)
+            speech_message = InternalMessage(
+                to=settings.agent_settings.robot_speech_name,
+                sender=settings.agent_settings.bdi_core_name,
+                body=speech_command.model_dump_json(),
+            )
+            # TODO: add to conversation history
+            self.add_behavior(self.send(speech_message))
             yield
 
         @self.actions.add(".gesture", 2)
@@ -336,8 +349,8 @@ class BDICoreAgent(BaseAgent):
         self.logger.info("Message sent to LLM agent: %s", text)
 
     @staticmethod
-    def format_belief_string(name: str, args: Iterable[str] = []):
+    def format_belief_string(name: str, args: Iterable[str] | None = []):
         """
         Given a belief's name and its args, return a string of the form "name(*args)"
         """
-        return f"{name}{'(' if args else ''}{','.join(args)}{')' if args else ''}"
+        return f"{name}{'(' if args else ''}{','.join(args or [])}{')' if args else ''}"

src/control_backend/agents/bdi/bdi_program_manager.py

@@ -1,598 +1,15 @@
-import uuid
-from collections.abc import Iterable
+import asyncio
 
 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 (
-    Action,
-    BasicBelief,
-    BasicNorm,
-    Belief,
-    ConditionalNorm,
-    GestureAction,
-    Goal,
-    InferredBelief,
-    KeywordBelief,
-    LLMAction,
-    LogicalOperator,
-    Phase,
-    Plan,
-    Program,
-    ProgramElement,
-    SemanticBelief,
-    SpeechAction,
-    Trigger,
-)
-
-test_program = Program(
-    phases=[
-        Phase(
-            norms=[
-                BasicNorm(norm="Talk like a pirate", id=uuid.uuid4()),
-                ConditionalNorm(
-                    condition=InferredBelief(
-                        left=KeywordBelief(keyword="Arr", id=uuid.uuid4()),
-                        right=SemanticBelief(
-                            description="testing", name="semantic belief", id=uuid.uuid4()
-                        ),
-                        operator=LogicalOperator.OR,
-                        name="Talking to a pirate",
-                        id=uuid.uuid4(),
-                    ),
-                    norm="Use nautical terms",
-                    id=uuid.uuid4(),
-                ),
-                ConditionalNorm(
-                    condition=SemanticBelief(
-                        description="We are talking to a child",
-                        name="talking to child",
-                        id=uuid.uuid4(),
-                    ),
-                    norm="Do not use cuss words",
-                    id=uuid.uuid4(),
-                ),
-            ],
-            triggers=[
-                Trigger(
-                    condition=InferredBelief(
-                        left=KeywordBelief(keyword="key", id=uuid.uuid4()),
-                        right=InferredBelief(
-                            left=KeywordBelief(keyword="key2", id=uuid.uuid4()),
-                            right=SemanticBelief(
-                                description="Decode this", name="semantic belief 2", id=uuid.uuid4()
-                            ),
-                            operator=LogicalOperator.OR,
-                            name="test trigger inferred inner",
-                            id=uuid.uuid4(),
-                        ),
-                        operator=LogicalOperator.OR,
-                        name="test trigger inferred outer",
-                        id=uuid.uuid4(),
-                    ),
-                    plan=Plan(
-                        steps=[
-                            SpeechAction(text="Testing trigger", id=uuid.uuid4()),
-                            Goal(
-                                name="Testing trigger",
-                                plan=Plan(
-                                    steps=[LLMAction(goal="Do something", id=uuid.uuid4())],
-                                    id=uuid.uuid4(),
-                                ),
-                                id=uuid.uuid4(),
-                            ),
-                        ],
-                        id=uuid.uuid4(),
-                    ),
-                    id=uuid.uuid4(),
-                )
-            ],
-            goals=[
-                Goal(
-                    name="Determine user age",
-                    plan=Plan(
-                        steps=[LLMAction(goal="Determine the age of the user.", id=uuid.uuid4())],
-                        id=uuid.uuid4(),
-                    ),
-                    id=uuid.uuid4(),
-                ),
-                Goal(
-                    name="Find the user's name",
-                    plan=Plan(
-                        steps=[
-                            Goal(
-                                name="Greet the user",
-                                plan=Plan(
-                                    steps=[LLMAction(goal="Greet the user.", id=uuid.uuid4())],
-                                    id=uuid.uuid4(),
-                                ),
-                                can_fail=False,
-                                id=uuid.uuid4(),
-                            ),
-                            Goal(
-                                name="Ask for name",
-                                plan=Plan(
-                                    steps=[
-                                        LLMAction(goal="Obtain the user's name.", id=uuid.uuid4())
-                                    ],
-                                    id=uuid.uuid4(),
-                                ),
-                                id=uuid.uuid4(),
-                            ),
-                        ],
-                        id=uuid.uuid4(),
-                    ),
-                    id=uuid.uuid4(),
-                ),
-                Goal(
-                    name="Tell a joke",
-                    plan=Plan(
-                        steps=[LLMAction(goal="Tell a joke.", id=uuid.uuid4())], id=uuid.uuid4()
-                    ),
-                    id=uuid.uuid4(),
-                ),
-            ],
-            id=uuid.uuid4(),
-        ),
-        Phase(
-            id=uuid.uuid4(),
-            norms=[
-                BasicNorm(norm="Use very gentle speech.", id=uuid.uuid4()),
-                ConditionalNorm(
-                    condition=SemanticBelief(
-                        description="We are talking to a child",
-                        name="talking to child",
-                        id=uuid.uuid4(),
-                    ),
-                    norm="Do not use cuss words",
-                    id=uuid.uuid4(),
-                ),
-            ],
-            triggers=[
-                Trigger(
-                    condition=InferredBelief(
-                        left=KeywordBelief(keyword="help", id=uuid.uuid4()),
-                        right=SemanticBelief(
-                            description="User is stuck", name="stuck", id=uuid.uuid4()
-                        ),
-                        operator=LogicalOperator.OR,
-                        name="help_or_stuck",
-                        id=uuid.uuid4(),
-                    ),
-                    plan=Plan(
-                        steps=[
-                            Goal(
-                                name="Unblock user",
-                                plan=Plan(
-                                    steps=[
-                                        LLMAction(
-                                            goal="Provide a step-by-step path to "
-                                            "resolve the user's issue.",
-                                            id=uuid.uuid4(),
-                                        )
-                                    ],
-                                    id=uuid.uuid4(),
-                                ),
-                                id=uuid.uuid4(),
-                            ),
-                        ],
-                        id=uuid.uuid4(),
-                    ),
-                    id=uuid.uuid4(),
-                ),
-            ],
-            goals=[
-                Goal(
-                    name="Clarify intent",
-                    plan=Plan(
-                        steps=[
-                            LLMAction(
-                                goal="Ask 1-2 targeted questions to clarify the "
-                                "user's intent, then proceed.",
-                                id=uuid.uuid4(),
-                            )
-                        ],
-                        id=uuid.uuid4(),
-                    ),
-                    id=uuid.uuid4(),
-                ),
-                Goal(
-                    name="Provide solution",
-                    plan=Plan(
-                        steps=[
-                            LLMAction(
-                                goal="Deliver a solution to complete the user's goal.",
-                                id=uuid.uuid4(),
-                            )
-                        ],
-                        id=uuid.uuid4(),
-                    ),
-                    id=uuid.uuid4(),
-                ),
-                Goal(
-                    name="Summarize next steps",
-                    plan=Plan(
-                        steps=[
-                            LLMAction(
-                                goal="Summarize what the user should do next.", id=uuid.uuid4()
-                            )
-                        ],
-                        id=uuid.uuid4(),
-                    ),
-                    id=uuid.uuid4(),
-                ),
-            ],
-        ),
-    ]
-)
-
-
-def do_things():
-    print(AgentSpeakGenerator().generate(test_program))
-
-
-class AgentSpeakGenerator:
-    """
-    Converts Pydantic representation of behavior programs into AgentSpeak(L) code string.
-    """
-
-    arrow_prefix = f"{' ' * 2}<-{' ' * 2}"
-    colon_prefix = f"{' ' * 2}:{' ' * 3}"
-    indent_prefix = " " * 6
-
-    def generate(self, program: Program) -> str:
-        lines = []
-        lines.append("")
-
-        lines += self._generate_initial_beliefs(program)
-
-        lines += self._generate_basic_flow(program)
-
-        lines += self._generate_phase_transitions(program)
-
-        lines += self._generate_norms(program)
-
-        lines += self._generate_belief_inference(program)
-
-        lines += self._generate_goals(program)
-
-        lines += self._generate_triggers(program)
-
-        return "\n".join(lines)
-
-    def _generate_initial_beliefs(self, program: Program) -> Iterable[str]:
-        yield "// --- Initial beliefs and agent startup ---"
-
-        yield "phase(start)."
-
-        yield ""
-
-        yield "+started"
-        yield f"{self.colon_prefix}phase(start)"
-        yield f"{self.arrow_prefix}phase({program.phases[0].id if program.phases else 'end'})."
-
-        yield from ["", ""]
-
-    def _generate_basic_flow(self, program: Program) -> Iterable[str]:
-        yield "// --- Basic flow ---"
-
-        for phase in program.phases:
-            yield from self._generate_basic_flow_per_phase(phase)
-
-        yield from ["", ""]
-
-    def _generate_basic_flow_per_phase(self, phase: Phase) -> Iterable[str]:
-        yield "+user_said(Message)"
-        yield f"{self.colon_prefix}phase({phase.id})"
-
-        goals = phase.goals
-        if goals:
-            yield f"{self.arrow_prefix}{self._slugify(goals[0], include_prefix=True)}"
-            for goal in goals[1:]:
-                yield f"{self.indent_prefix}{self._slugify(goal, include_prefix=True)}"
-
-        yield f"{self.indent_prefix if goals else self.arrow_prefix}!transition_phase."
-
-    def _generate_phase_transitions(self, program: Program) -> Iterable[str]:
-        yield "// --- Phase transitions ---"
-
-        if len(program.phases) == 0:
-            yield from ["", ""]
-            return
-
-        # TODO: remove outdated things
-
-        for i in range(-1, len(program.phases)):
-            predecessor = program.phases[i] if i >= 0 else None
-            successor = program.phases[i + 1] if i < len(program.phases) - 1 else None
-            yield from self._generate_phase_transition(predecessor, successor)
-
-        yield from self._generate_phase_transition(None, None)  # to avoid failing plan
-
-        yield from ["", ""]
-
-    def _generate_phase_transition(
-        self, phase: Phase | None = None, next_phase: Phase | None = None
-    ) -> Iterable[str]:
-        yield "+!transition_phase"
-
-        if phase is None and next_phase is None:  # base case true to avoid failing plan
-            yield f"{self.arrow_prefix}true."
-            return
-
-        yield f"{self.colon_prefix}phase({phase.id if phase else 'start'})"
-        yield f"{self.arrow_prefix}-+phase({next_phase.id if next_phase else 'end'})."
-
-    def _generate_norms(self, program: Program) -> Iterable[str]:
-        yield "// --- Norms ---"
-
-        for phase in program.phases:
-            for norm in phase.norms:
-                if type(norm) is BasicNorm:
-                    yield f"{self._slugify(norm)} :- phase({phase.id})."
-                if type(norm) is ConditionalNorm:
-                    yield (
-                        f"{self._slugify(norm)} :- phase({phase.id}) & "
-                        f"{self._slugify(norm.condition)}."
-                    )
-
-        yield from ["", ""]
-
-    def _generate_belief_inference(self, program: Program) -> Iterable[str]:
-        yield "// --- Belief inference rules ---"
-
-        for phase in program.phases:
-            for norm in phase.norms:
-                if not isinstance(norm, ConditionalNorm):
-                    continue
-
-                yield from self._belief_inference_recursive(norm.condition)
-
-            for trigger in phase.triggers:
-                yield from self._belief_inference_recursive(trigger.condition)
-
-        yield from ["", ""]
-
-    def _belief_inference_recursive(self, belief: Belief) -> Iterable[str]:
-        if type(belief) is KeywordBelief:
-            yield (
-                f"{self._slugify(belief)} :- user_said(Message) & "
-                f'.substring(Message, "{belief.keyword}", Pos) & Pos >= 0.'
-            )
-        if type(belief) is InferredBelief:
-            yield (
-                f"{self._slugify(belief)} :- {self._slugify(belief.left)} "
-                f"{'&' if belief.operator == LogicalOperator.AND else '|'} "
-                f"{self._slugify(belief.right)}."
-            )
-
-            yield from self._belief_inference_recursive(belief.left)
-            yield from self._belief_inference_recursive(belief.right)
-
-    def _generate_goals(self, program: Program) -> Iterable[str]:
-        yield "// --- Goals ---"
-
-        for phase in program.phases:
-            previous_goal: Goal | None = None
-            for goal in phase.goals:
-                yield from self._generate_goal_plan_recursive(goal, phase, previous_goal)
-                previous_goal = goal
-
-        yield from ["", ""]
-
-    def _generate_goal_plan_recursive(
-        self, goal: Goal, phase: Phase, previous_goal: Goal | None = None
-    ) -> Iterable[str]:
-        yield f"+{self._slugify(goal, include_prefix=True)}"
-
-        # Context
-        yield f"{self.colon_prefix}phase({phase.id}) &"
-        yield f"{self.indent_prefix}not responded_this_turn &"
-        yield f"{self.indent_prefix}not achieved_{self._slugify(goal)} &"
-        if previous_goal:
-            yield f"{self.indent_prefix}achieved_{self._slugify(previous_goal)}"
-        else:
-            yield f"{self.indent_prefix}true"
-
-        extra_goals_to_generate = []
-
-        steps = goal.plan.steps
-
-        if len(steps) == 0:
-            yield f"{self.arrow_prefix}true."
-            return
-
-        first_step = steps[0]
-        yield (
-            f"{self.arrow_prefix}{self._slugify(first_step, include_prefix=True)}"
-            f"{'.' if len(steps) == 1 and goal.can_fail else ';'}"
-        )
-        if isinstance(first_step, Goal):
-            extra_goals_to_generate.append(first_step)
-
-        for step in steps[1:-1]:
-            yield f"{self.indent_prefix}{self._slugify(step, include_prefix=True)};"
-            if isinstance(step, Goal):
-                extra_goals_to_generate.append(step)
-
-        if len(steps) > 1:
-            last_step = steps[-1]
-            yield (
-                f"{self.indent_prefix}{self._slugify(last_step, include_prefix=True)}"
-                f"{'.' if goal.can_fail else ';'}"
-            )
-            if isinstance(last_step, Goal):
-                extra_goals_to_generate.append(last_step)
-
-        if not goal.can_fail:
-            yield f"{self.indent_prefix}+achieved_{self._slugify(goal)}."
-
-        yield f"+{self._slugify(goal, include_prefix=True)}"
-        yield f"{self.arrow_prefix}true."
-
-        yield ""
-
-        extra_previous_goal: Goal | None = None
-        for extra_goal in extra_goals_to_generate:
-            yield from self._generate_goal_plan_recursive(extra_goal, phase, extra_previous_goal)
-            extra_previous_goal = extra_goal
-
-    def _generate_triggers(self, program: Program) -> Iterable[str]:
-        yield "// --- Triggers ---"
-
-        for phase in program.phases:
-            for trigger in phase.triggers:
-                yield from self._generate_trigger_plan(trigger, phase)
-
-        yield from ["", ""]
-
-    def _generate_trigger_plan(self, trigger: Trigger, phase: Phase) -> Iterable[str]:
-        belief_name = self._slugify(trigger.condition)
-
-        yield f"+{belief_name}"
-        yield f"{self.colon_prefix}phase({phase.id})"
-
-        extra_goals_to_generate = []
-
-        steps = trigger.plan.steps
-
-        if len(steps) == 0:
-            yield f"{self.arrow_prefix}true."
-            return
-
-        first_step = steps[0]
-        yield (
-            f"{self.arrow_prefix}{self._slugify(first_step, include_prefix=True)}"
-            f"{'.' if len(steps) == 1 else ';'}"
-        )
-        if isinstance(first_step, Goal):
-            extra_goals_to_generate.append(first_step)
-
-        for step in steps[1:-1]:
-            yield f"{self.indent_prefix}{self._slugify(step, include_prefix=True)};"
-            if isinstance(step, Goal):
-                extra_goals_to_generate.append(step)
-
-        if len(steps) > 1:
-            last_step = steps[-1]
-            yield f"{self.indent_prefix}{self._slugify(last_step, include_prefix=True)}."
-            if isinstance(last_step, Goal):
-                extra_goals_to_generate.append(last_step)
-
-        yield ""
-
-        extra_previous_goal: Goal | None = None
-        for extra_goal in extra_goals_to_generate:
-            yield from self._generate_trigger_plan_recursive(extra_goal, phase, extra_previous_goal)
-            extra_previous_goal = extra_goal
-
-    def _generate_trigger_plan_recursive(
-        self, goal: Goal, phase: Phase, previous_goal: Goal | None = None
-    ) -> Iterable[str]:
-        yield f"+{self._slugify(goal, include_prefix=True)}"
-
-        extra_goals_to_generate = []
-
-        steps = goal.plan.steps
-
-        if len(steps) == 0:
-            yield f"{self.arrow_prefix}true."
-            return
-
-        first_step = steps[0]
-        yield (
-            f"{self.arrow_prefix}{self._slugify(first_step, include_prefix=True)}"
-            f"{'.' if len(steps) == 1 and goal.can_fail else ';'}"
-        )
-        if isinstance(first_step, Goal):
-            extra_goals_to_generate.append(first_step)
-
-        for step in steps[1:-1]:
-            yield f"{self.indent_prefix}{self._slugify(step, include_prefix=True)};"
-            if isinstance(step, Goal):
-                extra_goals_to_generate.append(step)
-
-        if len(steps) > 1:
-            last_step = steps[-1]
-            yield (
-                f"{self.indent_prefix}{self._slugify(last_step, include_prefix=True)}"
-                f"{'.' if goal.can_fail else ';'}"
-            )
-            if isinstance(last_step, Goal):
-                extra_goals_to_generate.append(last_step)
-
-        if not goal.can_fail:
-            yield f"{self.indent_prefix}+achieved_{self._slugify(goal)}."
-
-        yield f"+{self._slugify(goal, include_prefix=True)}"
-        yield f"{self.arrow_prefix}true."
-
-        yield ""
-
-        extra_previous_goal: Goal | None = None
-        for extra_goal in extra_goals_to_generate:
-            yield from self._generate_goal_plan_recursive(extra_goal, phase, extra_previous_goal)
-            extra_previous_goal = extra_goal
-
-    def _slugify(self, element: ProgramElement, include_prefix: bool = False) -> str:
-        def base_slugify_call(text: str):
-            return slugify(text, separator="_", stopwords=["a", "the"])
-
-        if type(element) is KeywordBelief:
-            return f'keyword_said("{element.keyword}")'
-
-        if type(element) is SemanticBelief:
-            name = element.name
-            return f"semantic_{base_slugify_call(name if name else element.description)}"
-
-        if isinstance(element, BasicNorm):
-            return f'norm("{element.norm}")'
-
-        if isinstance(element, Goal):
-            return f"{'!' if include_prefix else ''}{base_slugify_call(element.name)}"
-
-        if isinstance(element, SpeechAction):
-            return f'.say("{element.text}")'
-
-        if isinstance(element, GestureAction):
-            return f'.gesture("{element.gesture}")'
-
-        if isinstance(element, LLMAction):
-            return f'!generate_response_with_goal("{element.goal}")'
-
-        if isinstance(element, Action.__value__):
-            raise NotImplementedError(
-                "Have not implemented an ASL string representation for this action."
-            )
-
-        if element.name == "":
-            raise ValueError("Name must be initialized for this type of ProgramElement.")
-
-        return base_slugify_call(element.name)
-
-    def _extract_basic_beliefs_from_program(self, program: Program) -> list[BasicBelief]:
-        beliefs = []
-
-        for phase in program.phases:
-            for norm in phase.norms:
-                if isinstance(norm, ConditionalNorm):
-                    beliefs += self._extract_basic_beliefs_from_belief(norm.condition)
-
-            for trigger in phase.triggers:
-                beliefs += self._extract_basic_beliefs_from_belief(trigger.condition)
-
-        return beliefs
-
-    def _extract_basic_beliefs_from_belief(self, belief: Belief) -> list[BasicBelief]:
-        if isinstance(belief, InferredBelief):
-            return self._extract_basic_beliefs_from_belief(
-                belief.left
-            ) + self._extract_basic_beliefs_from_belief(belief.right)
-        return [belief]
+from control_backend.schemas.belief_list import BeliefList, GoalList
+from control_backend.schemas.internal_message import InternalMessage
+from control_backend.schemas.program import Belief, ConditionalNorm, Goal, InferredBelief, Program
 
 
 class BDIProgramManager(BaseAgent):
@@ -611,40 +28,114 @@ class BDIProgramManager(BaseAgent):
         super().__init__(**kwargs)
         self.sub_socket = None
 
-    # async def _send_to_bdi(self, program: Program):
-    #     """
-    #     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:
-    #     -   **Norms**: Constraints or rules the agent must follow.
-    #     -   **Goals**: Objectives the agent must achieve.
-    #
-    #     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.
-    #
-    #     :param program: The program object received from the API.
-    #     """
-    #     first_phase = program.phases[0]
-    #     norms_belief = Belief(
-    #         name="norms",
-    #         arguments=[norm.norm for norm in first_phase.norms],
-    #         replace=True,
-    #     )
-    #     goals_belief = Belief(
-    #         name="goals",
-    #         arguments=[goal.description for goal in first_phase.goals],
-    #         replace=True,
-    #     )
-    #     program_beliefs = BeliefMessage(beliefs=[norms_belief, goals_belief])
-    #
-    #     message = InternalMessage(
-    #         to=settings.agent_settings.bdi_core_name,
-    #         sender=self.name,
-    #         body=program_beliefs.model_dump_json(),
-    #         thread="beliefs",
-    #     )
-    #     await self.send(message)
-    #     self.logger.debug("Sent new norms and goals to the BDI agent.")
+    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.
+
+        Currently, it takes the **first phase** of the program and extracts:
+        -   **Norms**: Constraints or rules the agent must follow.
+        -   **Goals**: Objectives the agent must achieve.
+
+        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.
+
+        :param program: The program object received from the API.
+        """
+        asg = AgentSpeakGenerator()
+
+        asl_str = asg.generate(program)
+
+        file_name = "src/control_backend/agents/bdi/agentspeak.asl"
+
+        with open(file_name, "w") as f:
+            f.write(asl_str)
+
+        msg = InternalMessage(
+            sender=self.name,
+            to=settings.agent_settings.bdi_core_name,
+            body=file_name,
+            thread="new_program",
+        )
+
+        await self.send(msg)
+
+    @staticmethod
+    def _extract_beliefs_from_program(program: Program) -> list[Belief]:
+        beliefs: list[Belief] = []
+
+        def extract_beliefs_from_belief(belief: Belief) -> list[Belief]:
+            if isinstance(belief, InferredBelief):
+                return extract_beliefs_from_belief(belief.left) + extract_beliefs_from_belief(
+                    belief.right
+                )
+            return [belief]
+
+        for phase in program.phases:
+            for norm in phase.norms:
+                if isinstance(norm, ConditionalNorm):
+                    beliefs += extract_beliefs_from_belief(norm.condition)
+
+            for trigger in phase.triggers:
+                beliefs += extract_beliefs_from_belief(trigger.condition)
+
+        return beliefs
+
+    async def _send_beliefs_to_semantic_belief_extractor(self, program: Program):
+        """
+        Extract beliefs from the program and send them to the Semantic Belief Extractor Agent.
+
+        :param program: The program received from the API.
+        """
+        beliefs = BeliefList(beliefs=self._extract_beliefs_from_program(program))
+
+        message = InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            sender=self.name,
+            body=beliefs.model_dump_json(),
+            thread="beliefs",
+        )
+
+        await self.send(message)
+
+    @staticmethod
+    def _extract_goals_from_program(program: Program) -> list[Goal]:
+        """
+        Extract all goals from the program, including subgoals.
+
+        :param program: The program received from the API.
+        :return: A list of Goal objects.
+        """
+        goals: list[Goal] = []
+
+        def extract_goals_from_goal(goal_: Goal) -> list[Goal]:
+            goals_: list[Goal] = [goal]
+            for plan in goal_.plan:
+                if isinstance(plan, Goal):
+                    goals_.extend(extract_goals_from_goal(plan))
+            return goals_
+
+        for phase in program.phases:
+            for goal in phase.goals:
+                goals.extend(extract_goals_from_goal(goal))
+
+        return goals
+
+    async def _send_goals_to_semantic_belief_extractor(self, program: Program):
+        """
+        Extract goals from the program and send them to the Semantic Belief Extractor Agent.
+
+        :param program: The program received from the API.
+        """
+        goals = GoalList(goals=self._extract_goals_from_program(program))
+
+        message = InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            sender=self.name,
+            body=goals.model_dump_json(),
+            thread="goals",
+        )
+
+        await self.send(message)
 
     async def _receive_programs(self):
         """
@@ -662,7 +153,11 @@ class BDIProgramManager(BaseAgent):
                 self.logger.exception("Received an invalid program.")
                 continue
 
-            await self._send_to_bdi(program)
+            await asyncio.gather(
+                self._create_agentspeak_and_send_to_bdi(program),
+                self._send_beliefs_to_semantic_belief_extractor(program),
+                self._send_goals_to_semantic_belief_extractor(program),
+            )
 
     async def setup(self):
         """
@@ -678,7 +173,3 @@ class BDIProgramManager(BaseAgent):
         self.sub_socket.subscribe("program")
 
         self.add_behavior(self._receive_programs())
-
-
-if __name__ == "__main__":
-    do_things()

src/control_backend/agents/bdi/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

@@ -2,22 +2,45 @@ import asyncio
 import json
 
 import httpx
-from pydantic import ValidationError
-from slugify import slugify
+from pydantic import BaseModel, ValidationError
 
 from control_backend.agents.base import BaseAgent
+from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
+from control_backend.schemas.belief_list import BeliefList, GoalList
 from control_backend.schemas.belief_message import Belief as InternalBelief
 from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.chat_history import ChatHistory, ChatMessage
-from control_backend.schemas.program import (
-    Belief,
-    ConditionalNorm,
-    InferredBelief,
-    Program,
-    SemanticBelief,
-)
+from control_backend.schemas.program import Goal, SemanticBelief
+
+type JSONLike = None | bool | int | float | str | list["JSONLike"] | dict[str, "JSONLike"]
+
+
+class BeliefState(BaseModel):
+    true: set[InternalBelief] = set()
+    false: set[InternalBelief] = set()
+
+    def difference(self, other: "BeliefState") -> "BeliefState":
+        return BeliefState(
+            true=self.true - other.true,
+            false=self.false - other.false,
+        )
+
+    def union(self, other: "BeliefState") -> "BeliefState":
+        return BeliefState(
+            true=self.true | other.true,
+            false=self.false | other.false,
+        )
+
+    def __sub__(self, other):
+        return self.difference(other)
+
+    def __or__(self, other):
+        return self.union(other)
+
+    def __bool__(self):
+        return bool(self.true) or bool(self.false)
 
 
 class TextBeliefExtractorAgent(BaseAgent):
@@ -34,8 +57,11 @@ class TextBeliefExtractorAgent(BaseAgent):
 
     def __init__(self, name: str):
         super().__init__(name)
-        self.beliefs: dict[str, bool] = {}
-        self.available_beliefs: list[SemanticBelief] = []
+        self._llm = self.LLM(self, settings.llm_settings.n_parallel)
+        self.belief_inferrer = SemanticBeliefInferrer(self._llm)
+        self.goal_inferrer = GoalAchievementInferrer(self._llm)
+        self._current_beliefs = BeliefState()
+        self._current_goal_completions: dict[str, bool] = {}
         self.conversation = ChatHistory(messages=[])
 
     async def setup(self):
@@ -57,8 +83,9 @@ class TextBeliefExtractorAgent(BaseAgent):
             case settings.agent_settings.transcription_name:
                 self.logger.debug("Received text from transcriber: %s", msg.body)
                 self._apply_conversation_message(ChatMessage(role="user", content=msg.body))
-                await self._infer_new_beliefs()
                 await self._user_said(msg.body)
+                await self._infer_new_beliefs()
+                await self._infer_goal_completions()
             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))
@@ -80,49 +107,60 @@ class TextBeliefExtractorAgent(BaseAgent):
 
     def _handle_program_manager_message(self, msg: InternalMessage):
         """
-        Handle a message from the program manager: extract available beliefs from it.
+        Handle a message from the program manager: extract available beliefs and goals from it.
 
         :param msg: The received message from the program manager.
         """
+        match msg.thread:
+            case "beliefs":
+                self._handle_beliefs_message(msg)
+            case "goals":
+                self._handle_goals_message(msg)
+            case "conversation_history":
+                if msg.body == "reset":
+                    self._reset()
+            case _:
+                self.logger.warning("Received unexpected message from %s", msg.sender)
+
+    def _reset(self):
+        self.conversation = ChatHistory(messages=[])
+        self.belief_inferrer.available_beliefs.clear()
+        self._current_beliefs = BeliefState()
+        self.goal_inferrer.goals.clear()
+        self._current_goal_completions = {}
+
+    def _handle_beliefs_message(self, msg: InternalMessage):
         try:
-            program = Program.model_validate_json(msg.body)
+            belief_list = BeliefList.model_validate_json(msg.body)
         except ValidationError:
             self.logger.warning(
-                "Received message from program manager but it is not a valid program."
+                "Received message from program manager but it is not a valid list of beliefs."
             )
             return
 
-        self.logger.debug("Received a program from the program manager.")
+        available_beliefs = [b for b in belief_list.beliefs if isinstance(b, SemanticBelief)]
+        self.belief_inferrer.available_beliefs = available_beliefs
+        self.logger.debug(
+            "Received %d semantic beliefs from the program manager.",
+            len(available_beliefs),
+        )
 
-        self.available_beliefs = self._extract_basic_beliefs_from_program(program)
+    def _handle_goals_message(self, msg: InternalMessage):
+        try:
+            goals_list = GoalList.model_validate_json(msg.body)
+        except ValidationError:
+            self.logger.warning(
+                "Received message from program manager but it is not a valid list of goals."
+            )
+            return
 
-    # 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]
+        # Use only goals that can fail, as the others are always assumed to be completed
+        available_goals = [g for g in goals_list.goals if g.can_fail]
+        self.goal_inferrer.goals = available_goals
+        self.logger.debug(
+            "Received %d failable goals from the program manager.",
+            len(available_goals),
+        )
 
     async def _user_said(self, text: str):
         """
@@ -130,126 +168,210 @@ class TextBeliefExtractorAgent(BaseAgent):
 
         :param text: User's transcribed text.
         """
-        belief = {"beliefs": {"user_said": [text]}, "type": "belief_extraction_text"}
-        payload = json.dumps(belief)
-
         belief_msg = InternalMessage(
-            to=settings.agent_settings.bdi_belief_collector_name,
+            to=settings.agent_settings.bdi_core_name,
             sender=self.name,
-            body=payload,
+            body=BeliefMessage(
+                replace=[InternalBelief(name="user_said", arguments=[text])],
+            ).model_dump_json(),
             thread="beliefs",
         )
         await self.send(belief_msg)
 
     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:
+        conversation_beliefs = await self.belief_inferrer.infer_from_conversation(self.conversation)
+
+        new_beliefs = conversation_beliefs - self._current_beliefs
+        if not new_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._current_beliefs |= new_beliefs
 
-            self.beliefs[belief_key] = belief_value
+        belief_changes = BeliefMessage(
+            create=list(new_beliefs.true),
+            delete=list(new_beliefs.false),
+        )
 
-            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(
+        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)
+        await self.send(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_goal_completions(self):
+        goal_completions = await self.goal_inferrer.infer_from_conversation(self.conversation)
 
-    async def _infer_turn(self) -> dict:
+        new_achieved = [
+            InternalBelief(name=goal, arguments=None)
+            for goal, achieved in goal_completions.items()
+            if achieved and self._current_goal_completions.get(goal) != achieved
+        ]
+        new_not_achieved = [
+            InternalBelief(name=goal, arguments=None)
+            for goal, achieved in goal_completions.items()
+            if not achieved and self._current_goal_completions.get(goal) != achieved
+        ]
+        for goal, achieved in goal_completions.items():
+            self._current_goal_completions[goal] = achieved
+
+        if not new_achieved and not new_not_achieved:
+            return
+
+        belief_changes = BeliefMessage(
+            create=new_achieved,
+            delete=new_not_achieved,
+        )
+        message = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
+            sender=self.name,
+            body=belief_changes.model_dump_json(),
+            thread="beliefs",
+        )
+        await self.send(message)
+
+    class LLM:
         """
-        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``.
+        Class that handles sending structured generation requests to an LLM.
         """
+
+        def __init__(self, agent: "TextBeliefExtractorAgent", n_parallel: int):
+            self._agent = agent
+            self._semaphore = asyncio.Semaphore(n_parallel)
+
+        async def query(self, prompt: str, schema: dict, tries: int = 3) -> JSONLike | None:
+            """
+            Query the LLM with the given prompt and schema, return an instance of a dict conforming
+            to this schema. Try ``tries`` times, or return None.
+
+            :param prompt: Prompt to be queried.
+            :param schema: Schema to be queried.
+            :param tries: Number of times to try to query the LLM.
+            :return: An instance of a dict conforming to this schema, or None if failed.
+            """
+            try_count = 0
+            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._agent.logger.exception(
+                        "Failed to get LLM response after %d tries.",
+                        try_count,
+                        exc_info=e,
+                    )
+
+            return None
+
+        async def _query_llm(self, prompt: str, schema: dict) -> JSONLike:
+            """
+            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 self._semaphore:
+                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=30.0,
+                    )
+                    response.raise_for_status()
+
+                    response_json = response.json()
+                    json_message = response_json["choices"][0]["message"]["content"]
+                    return json.loads(json_message)
+
+
+class SemanticBeliefInferrer:
+    """
+    Class that handles only prompting an LLM for semantic beliefs.
+    """
+
+    def __init__(
+        self,
+        llm: "TextBeliefExtractorAgent.LLM",
+        available_beliefs: list[SemanticBelief] | None = None,
+    ):
+        self._llm = llm
+        self.available_beliefs: list[SemanticBelief] = available_beliefs or []
+
+    async def infer_from_conversation(self, conversation: ChatHistory) -> BeliefState:
+        """
+        Process conversation history to extract beliefs, semantically. The result is an object that
+        describes all beliefs that hold or don't hold based on the full conversation.
+
+        :param conversation: The conversation history to be processed.
+        :return: An object that describes beliefs.
+        """
+        # Return instantly if there are no beliefs to infer
+        if not self.available_beliefs:
+            return BeliefState()
+
         n_parallel = max(1, min(settings.llm_settings.n_parallel - 1, len(self.available_beliefs)))
-        all_beliefs = await asyncio.gather(
+        all_beliefs: list[dict[str, bool | None] | None] = await asyncio.gather(
             *[
-                self._infer_beliefs(self.conversation, beliefs)
+                self._infer_beliefs(conversation, beliefs)
                 for beliefs in self._split_into_chunks(self.available_beliefs, n_parallel)
             ]
         )
-        retval = {}
+        retval = BeliefState()
         for beliefs in all_beliefs:
             if beliefs is None:
                 continue
-            retval.update(beliefs)
+            for belief_name, belief_holds in beliefs.items():
+                if belief_holds is None:
+                    continue
+                belief = InternalBelief(name=belief_name, arguments=None)
+                if belief_holds:
+                    retval.true.add(belief)
+                else:
+                    retval.false.add(belief)
         return retval
 
     @staticmethod
-    def _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,
-        }
+    def _split_into_chunks[T](items: list[T], n: int) -> list[list[T]]:
+        """
+        Split a list into ``n`` chunks, making each chunk approximately ``len(items) / n`` long.
 
-    @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
-            ]
-        )
+        :param items: The list of items to split.
+        :param n: The number of desired chunks.
+        :return: A list of chunks each approximately ``len(items) / n`` long.
+        """
+        k, m = divmod(len(items), n)
+        return [items[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)]
 
     async def _infer_beliefs(
         self,
         conversation: ChatHistory,
         beliefs: list[SemanticBelief],
-    ) -> dict | None:
+    ) -> dict[str, bool | None] | None:
         """
         Infer given beliefs based on the given conversation.
         :param conversation: The conversation to infer beliefs from.
@@ -267,7 +389,7 @@ 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):
+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:
@@ -276,70 +398,79 @@ Respond with a JSON similar to the following, but with the property names as giv
 
         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
+        return await self._llm.query(prompt, schema)
 
     @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.
+    def _create_belief_schema(belief: SemanticBelief) -> tuple[str, dict]:
+        return AgentSpeakGenerator.slugify(belief), {
+            "type": ["boolean", "null"],
+            "description": belief.description,
+        }
 
-        :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()
+    @staticmethod
+    def _create_beliefs_schema(beliefs: list[SemanticBelief]) -> dict:
+        belief_schemas = [
+            SemanticBeliefInferrer._create_belief_schema(belief) for belief in beliefs
+        ]
 
-            response_json = response.json()
-            json_message = response_json["choices"][0]["message"]["content"]
-            return json.loads(json_message)
+        return {
+            "type": "object",
+            "properties": dict(belief_schemas),
+            "required": [name for name, _ in belief_schemas],
+        }
+
+    @staticmethod
+    def _format_message(message: ChatMessage):
+        return f"{message.role.upper()}:\n{message.content}"
+
+    @staticmethod
+    def _format_conversation(conversation: ChatHistory):
+        return "\n\n".join(
+            [SemanticBeliefInferrer._format_message(message) for message in conversation.messages]
+        )
+
+    @staticmethod
+    def _format_beliefs(beliefs: list[SemanticBelief]):
+        return "\n".join(
+            [f"- {AgentSpeakGenerator.slugify(belief)}: {belief.description}" for belief in beliefs]
+        )
+
+
+class GoalAchievementInferrer(SemanticBeliefInferrer):
+    def __init__(self, llm: TextBeliefExtractorAgent.LLM):
+        super().__init__(llm)
+        self.goals = []
+
+    async def infer_from_conversation(self, conversation: ChatHistory) -> dict[str, bool]:
+        """
+        Determine which goals have been achieved based on the given conversation.
+
+        :param conversation: The conversation to infer goal completion from.
+        :return: A mapping of goals and a boolean whether they have been achieved.
+        """
+        if not self.goals:
+            return {}
+
+        goals_achieved = await asyncio.gather(
+            *[self._infer_goal(conversation, g) for g in self.goals]
+        )
+        return {
+            f"achieved_{AgentSpeakGenerator.slugify(goal)}": achieved
+            for goal, achieved in zip(self.goals, goals_achieved, strict=True)
+        }
+
+    async def _infer_goal(self, conversation: ChatHistory, goal: Goal) -> bool:
+        prompt = f"""{self._format_conversation(conversation)}
+
+Given the above conversation, what has the following goal been achieved?
+
+The name of the goal: {goal.name}
+Description of the goal: {goal.description}
+
+Answer with literally only `true` or `false` (without backticks)."""
+
+        schema = {
+            "type": "boolean",
+        }
+
+        return await self._llm.query(prompt, schema)

src/control_backend/agents/communication/ri_communication_agent.py

@@ -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
@@ -301,11 +298,3 @@ 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/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

@@ -6,12 +6,7 @@ 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,
-)
+from control_backend.schemas.ri_message import GestureCommand, RIEndpoint, SpeechCommand
 
 
 class UserInterruptAgent(BaseAgent):
@@ -76,12 +71,6 @@ class UserInterruptAgent(BaseAgent):
                     "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').",
@@ -141,38 +130,6 @@ class UserInterruptAgent(BaseAgent):
             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.

src/control_backend/core/agent_system.py

@@ -192,7 +192,16 @@ class BaseAgent(ABC):
 
         :param coro: The coroutine to execute as a task.
         """
-        task = asyncio.create_task(coro)
+
+        async def try_coro(coro_: Coroutine):
+            try:
+                await coro_
+            except asyncio.CancelledError:
+                self.logger.debug("A behavior was canceled successfully: %s", coro_)
+            except Exception:
+                self.logger.warning("An exception occurred in a behavior.", exc_info=True)
+
+        task = asyncio.create_task(try_coro(coro))
         self._tasks.add(task)
         task.add_done_callback(self._tasks.discard)
         return task

src/control_backend/main.py

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

src/control_backend/schemas/belief_list.py

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

src/control_backend/schemas/belief_message.py

@@ -13,6 +13,9 @@ class Belief(BaseModel):
     name: str
     arguments: list[str] | None
 
+    # To make it hashable
+    model_config = {"frozen": True}
+
 
 class BeliefMessage(BaseModel):
     """

src/control_backend/schemas/program.py

@@ -43,7 +43,6 @@ class SemanticBelief(ProgramElement):
     :ivar description: Description of how to form the belief, used by the LLM.
     """
 
-    name: str = ""
     description: str
 
 
@@ -113,10 +112,12 @@ class Goal(ProgramElement):
     for example when the achieving of the goal is dependent on the user's reply, this means
     that the achieved status will be set from somewhere else in the program.
 
+    :ivar description: A description of the goal, used to determine if it has been achieved.
     :ivar plan: The plan to execute.
     :ivar can_fail: Whether we can fail to achieve the goal after executing the plan.
     """
 
+    description: str = ""
     plan: Plan
     can_fail: bool = True
 

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):
@@ -65,14 +64,3 @@ 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/bdi/test_bdi_core_agent.py

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

test/unit/agents/bdi/test_bdi_program_manager.py

@@ -32,6 +32,8 @@ def make_valid_program_json(norm="N1", goal="G1") -> str:
                     Goal(
                         id=uuid.uuid4(),
                         name=goal,
+                        description="This description can be used to determine whether the goal "
+                        "has been achieved.",
                         plan=Plan(
                             id=uuid.uuid4(),
                             name="Goal Plan",
@@ -53,7 +55,7 @@ async def test_send_to_bdi():
     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]
@@ -75,8 +77,9 @@ async def test_receive_programs_valid_and_invalid():
     ]
 
     manager = BDIProgramManager(name="program_manager_test")
+    manager._internal_pub_socket = AsyncMock()
     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 +88,7 @@ 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
-    forwarded: Program = manager._send_to_bdi.await_args[0][0]
+    assert manager._create_agentspeak_and_send_to_bdi.await_count == 1
+    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].goals[0].name == "G1"

test/unit/agents/bdi/test_text_belief_extractor.py

@@ -8,9 +8,11 @@ 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_list import BeliefList
 from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.program import (
     ConditionalNorm,
+    KeywordBelief,
     LLMAction,
     Phase,
     Plan,
@@ -186,13 +188,31 @@ async def test_retry_query_llm_fail_immediately(agent):
 
 
 @pytest.mark.asyncio
-async def test_extracting_beliefs_from_program(agent, sample_program):
+async def test_extracting_semantic_beliefs(agent):
+    """
+    The Program Manager sends beliefs to this agent. Test whether the agent handles them correctly.
+    """
     assert len(agent.available_beliefs) == 0
+    beliefs = BeliefList(
+        beliefs=[
+            KeywordBelief(
+                id=uuid.uuid4(),
+                name="keyword_hello",
+                keyword="hello",
+            ),
+            SemanticBelief(
+                id=uuid.uuid4(), name="semantic_hello_1", description="Some semantic belief 1"
+            ),
+            SemanticBelief(
+                id=uuid.uuid4(), name="semantic_hello_2", description="Some semantic belief 2"
+            ),
+        ]
+    )
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
             sender=settings.agent_settings.bdi_program_manager_name,
-            body=sample_program.model_dump_json(),
+            body=beliefs.model_dump_json(),
         ),
     )
     assert len(agent.available_beliefs) == 2

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

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

test/unit/schemas/test_ui_program_message.py

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