feat: (hopefully) optional norms and goals

ref: N25B-200

src/control_backend/agents/bdi/bdi_core.py

@@ -45,17 +45,45 @@ class BDICoreAgent(BDIAgent):
             Example: .reply("Hello LLM!")
             """
             message_text = agentspeak.grounded(term.args[0], intention.scope)
-            norm = agentspeak.grounded(term.args[1], intention.scope)
-            goal = agentspeak.grounded(term.args[2], intention.scope)
+            norms = agentspeak.grounded(term.args[1], intention.scope)
+            goals = agentspeak.grounded(term.args[2], intention.scope)
 
-            self.logger.debug("Norms: %s", norm)
-            self.logger.debug("Goals: %s", goal)
-            self.logger.debug("Reply action sending: %s", message_text)
+            self.logger.debug("Norms: %s", norms)
+            self.logger.debug("Goals: %s", goals)
+            self.logger.debug("User text: %s", message_text)
 
-            self._send_to_llm(str(message_text), str(norm), str(goal))
+            self._send_to_llm(str(message_text), str(norms), str(goals))
             yield
 
-    def _send_to_llm(self, text: str, norm: str, goal: str):
+        @actions.add(".reply_no_norms", 2)
+        def _reply_no_norms(agent: "BDICoreAgent", term, intention):
+            message_text = agentspeak.grounded(term.args[0], intention.scope)
+            goals = agentspeak.grounded(term.args[1], intention.scope)
+
+            self.logger.debug("Goals: %s", goals)
+            self.logger.debug("User text: %s", message_text)
+
+            self._send_to_llm(str(message_text), goals=str(goals))
+
+        @actions.add(".reply_no_goals", 2)
+        def _reply_no_goals(agent: "BDICoreAgent", term, intention):
+            message_text = agentspeak.grounded(term.args[0], intention.scope)
+            norms = agentspeak.grounded(term.args[1], intention.scope)
+
+            self.logger.debug("Norms: %s", norms)
+            self.logger.debug("User text: %s", message_text)
+
+            self._send_to_llm(str(message_text), norms=str(norms))
+
+        @actions.add(".reply_no_goals_no_norms", 1)
+        def _reply_no_goals_no_norms(agent: "BDICoreAgent", term, intention):
+            message_text = agentspeak.grounded(term.args[0], intention.scope)
+
+            self.logger.debug("User text: %s", message_text)
+
+            self._send_to_llm(message_text)
+
+    def _send_to_llm(self, text: str, norms: str = None, goals: str = None):
         """
         Sends a text query to the LLM Agent asynchronously.
         """
@@ -64,8 +92,8 @@ class BDICoreAgent(BDIAgent):
             async def run(self) -> None:
                 message_dict = {
                     "text": text,
-                    "norms": norm,
-                    "goals": goal,
+                    "norms": norms if norms else "",
+                    "goals": goals if goals else "",
                 }
                 msg = Message(
                     to=settings.agent_settings.llm_agent_name + "@" + settings.agent_settings.host,

src/control_backend/agents/bdi/rules.asl

@@ -1,6 +1,15 @@
-norms(test_norm).
-goals(test_goal).
-
 +new_message : user_said(Message) & norms(Norms) & goals(Goals) <-
   -new_message;
   .reply(Message, Norms, Goals).
+
++new_message : user_said(Message) & norms(Norms) <-
+  -new_message;
+  .reply_no_goals(Message, Norms).
+
++new_message : user_said(Message) & goals(Goals) <-
+  -new_message;
+  .reply_no_norms(Message, Goals).
+
++new_message : user_said(Message) <-
+  -new_message;
+  .reply_no_goals_no_norms(Message).

src/control_backend/agents/llm/llm.py

@@ -82,7 +82,7 @@ class LLMAgent(BaseAgent):
             :param prompt: Input text prompt to pass to the LLM.
             :yield: Fragments of the LLM-generated content.
             """
-            instructions = LLMInstructions(norms, goals)
+            instructions = LLMInstructions(norms if norms else None, goals if goals else None)
             messages = [
                 {
                     "role": "developer",