up

/n/Users/davidccavalcante/Takk/Hub/projects/TeleologyHI/HIM-self/src/core/emotional_intelligence.py

@@ -0,0 +1,61 @@
+class EmotionalProcessor:
+    def __init__(self):
+        self.emotion_encoder = nn.Sequential(
+            nn.Linear(768, 256),
+            nn.ReLU(),
+            nn.Linear(256, 128)
+        )
+        self.emotional_memory = {}
+        self.emotion_vectors = self._initialize_emotion_vectors()
+        
+    async def process_emotional_context(self, input_data: Dict[str, Any]) -> EmotionalState:
+        """Process emotional context to extract emotional state."""
+        # Extract context features
+        context_vector = await self._encode_context(input_data)
+        
+        # Analyze emotional content
+        emotions = await self._analyze_emotions(context_vector)
+        
+        # Update emotional memory
+        await self._update_emotional_memory(emotions)
+        
+        return EmotionalState(
+            valence=emotions.get("valence", 0.0),
+            arousal=emotions.get("arousal", 0.0),
+            dominance=emotions.get("dominance", 0.0),
+            emotions=list(emotions.get("categories", [])),
+            intensity=emotions.get("intensities", {})
+        )
+
+    def _initialize_emotion_vectors(self) -> Dict[str, List[float]]:
+        """Initialize base emotion vectors."""
+        return {
+            "joy": [0.8, 0.7, 0.6],
+            "sadness": [-0.7, -0.3, -0.4],
+            "anger": [-0.6, 0.8, 0.7],
+            "fear": [-0.8, 0.5, -0.7],
+            "surprise": [0.4, 0.8, 0.1],
+            "disgust": [-0.8, 0.2, 0.3]
+        }
+        
+    async def _encode_context(self, input_data: Dict[str, Any]) -> torch.Tensor:
+        """Encode input context into emotion space."""
+        # Placeholder: Convert input data to tensor
+        context_tensor = torch.randn(768)  # Simulated input tensor
+        return self.emotion_encoder(context_tensor)
+        
+    async def _analyze_emotions(self, context_vector: torch.Tensor) -> Dict[str, Any]:
+        """Analyze encoded context for emotional content."""
+        # Placeholder: Emotion analysis logic
+        return {
+            "valence": 0.7,
+            "arousal": 0.6,
+            "dominance": 0.5,
+            "categories": ["joy", "surprise"],
+            "intensities": {"joy": 0.8, "surprise": 0.4}
+        }
+        
+    async def _update_emotional_memory(self, emotions: Dict[str, Any]):
+        """Update emotional memory with new emotional state."""
+        # Placeholder: Memory update logic
+        self.emotional_memory["last_state"] = emotions

/n/Users/davidccavalcante/Takk/Hub/projects/TeleologyHI/HIM-self/src/core/theory_of_mind.py

@@ -0,0 +1,64 @@
+from typing import Dict, Any, List
+import torch
+import torch.nn as nn
+import asyncio
+import numpy as np
+    async def model_agent_mind(self,
+                        agent_data: Dict[str, Any],
+                        context: Dict[str, Any] = None) -> Dict[str, Any]:
+        """Model the mental state, beliefs, and behavior of an agent."""
+        # Process agent data to infer mental state
+        mental_state = await self._process_mental_state(agent_data)
+        
+        # Update belief system based on mental state and context
+        beliefs = await self._update_belief_system(mental_state, context)
+        
+        # Predict future behavior based on mental state and beliefs
+        behavior = await self._predict_behavior(mental_state, beliefs)
+        
+        return {
+            'mental_state': mental_state,
+            'beliefs': beliefs,
+            'predicted_behavior': behavior
+        }
+        
+    async def _process_mental_state(self, agent_data: Dict[str, Any]) -> Dict[str, float]:
+        """Process agent data to infer mental state."""
+        # Convert input data to tensor
+        agent_tensor = torch.randn(768)  # Simulated input tensor
+        
+        # Process through neural network
+        mental_state_vector = self.mental_state_model(agent_tensor)
+        
+        # Convert to interpretable format
+        return {
+            "attention": float(mental_state_vector[0]),
+            "intention": float(mental_state_vector[1]),
+            "knowledge": float(mental_state_vector[2]),
+            "emotional_state": float(mental_state_vector[3])
+        }
+        
+    async def _update_belief_system(self, 
+                               mental_state: Dict[str, float], 
+                               context: Dict[str, Any] = None) -> Dict[str, Any]:
+        """Update belief system based on new mental state."""
+        beliefs = {
+            "current_goals": ["understand", "communicate"],
+            "knowledge_state": mental_state["knowledge"],
+            "attention_focus": mental_state["attention"],
+            "context_awareness": 0.8
+        }
+        
+        self.belief_system = beliefs
+        return beliefs
+        
+    async def _predict_behavior(self, 
+                           mental_state: Dict[str, float], 
+                           beliefs: Dict[str, Any]) -> Dict[str, float]:
+        """Predict future behavior based on mental state and beliefs."""
+        return {
+            "cooperation_likelihood": 0.8,
+            "communication_style": 0.7,
+            "decision_confidence": 0.9,
+            "response_time": 0.6
+        }

src/core/emotional_intelligence.py

@@ -1,6 +1,8 @@
 from typing import Dict, Any, List
 from dataclasses import dataclass
-
+import torch
+import torch.nn as nn
+import asyncio
 @dataclass
 class EmotionalState:
     valence: float  # Positive/negative dimension

src/model/him_model.py

@@ -17,9 +17,9 @@ class HIMModel(nn.Module):
         self.semiotic_processor = SemioticProcessor()
         
     async def generate_response(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
-        consciousness_state = self.consciousness_kernel.process_consciousness_cycle(input_data)
-        emotional_context = self.emotional_processor.process_emotional_context(input_data)
-        social_understanding = self.theory_of_mind.model_agent_mind(input_data)
+        consciousness_state = await self.consciousness_kernel.process_consciousness_cycle(input_data)
+        emotional_context = await self.emotional_processor.process_emotional_context(input_data)
+        social_understanding = await self.theory_of_mind.model_agent_mind(input_data)
         semiotic_analysis = await self.semiotic_processor.process(input_data)
         
         return self._integrate_outputs(