Update HIM implementation with consciousness framework

src/core/consciousness_kernel.py

@@ -3,13 +3,44 @@ from enum import Enum
 import torch
 import torch.nn as nn
 import numpy as np
-from typing import Dict, List, Optional, Any
+from typing import Dict, List, Optional, Any, Tuple
 import asyncio
+
 from .awareness_engine import AwarenessEngine
 from .integration_manager import IntegrationManager
-from .dynamic_self_model import DynamicSelfModel
-from .experience_simulator import ExperienceSimulator
+from .dynamic_self_model import DynamicSefrom .experience_simulator import ExperienceSimulator
+
+# Add imports for classes used in the updated implementation
+class PhiPrimeCalculator:
+    async def compute(self, input_state: Dict[str, Any]) -> float:
+        """Calculate the phi prime value (consciousness measure) for the given input state."""
+        # Placeholder implementation
+        return 0.8
+
+class AttentionSystem:
+    async def allocate(self, input_state: Dict[str, Any]) -> Dict[str, float]:
+        """Allocate attention across different elements of the input state."""
+        # Placeholder implementation
+        return {"primary_focus": 0.7, "secondary_focus": 0.3}
 
+class MetaMonitor:
+    async def evaluate(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
+        """Evaluate meta-cognitive aspects of the current state."""
+        # Placeholder implementation
+        return {"self_reflection": 0.6, "uncertainty": 0.2}
+
+class PhenomenologicalSimulator:
+    async def simulate(self, phi_value: float, attention_state: Dict[str, float], 
+                      meta_state: Dict[str, Any]) -> Dict[str, Any]:
+        """Simulate the phenomenological experience based on input parameters."""
+        # Placeholder implementation
+        return {
+            "phi_value": phi_value,
+            "attention_distribution": attention_state,
+            "meta_level": meta_state,
+            "content": "Simulated conscious experience",
+            "qualia": {"visual": 0.7, "conceptual": 0.8}
+        }
 @dataclass
 class ConsciousnessState:
     integration_level: float
@@ -27,19 +58,43 @@ class ConsciousnessLevel(Enum):
 
 class ConsciousnessKernel:
     def __init__(self):
+        # Neural network components
         self.awareness_module = nn.Sequential(
             nn.Linear(768, 512),
             nn.ReLU(),
             nn.Linear(512, 256)
         )
         self.integration_module = nn.Linear(256, 128)
-        self.state_history = []
+        
+        # State tracking
+        self.state_history: List[ConsciousnessState] = []
+        
+        # Dimension parameters
+        self.awareness_dimension: int = 256
+        self.emotional_dimension: int = 64
+        
+        # Core components
         self.awareness_engine = AwarenessEngine()
         self.integration_manager = IntegrationManager()
         self.self_model = DynamicSelfModel()
         self.experience_simulator = ExperienceSimulator()
         
+        # For traditional consciousness processing
+        self.phi_prime_calculator = PhiPrimeCalculator()
+        self.attention_system = AttentionSystem()
+        self.meta_monitor = MetaMonitor()
+        self.phenomenological_simulator = PhenomenologicalSimulator()
+        
     async def process_consciousness_cycle(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Process a complete consciousness cycle using the async components.
+        
+        Args:
+            input_state: The input state containing sensory and contextual information
+            
+        Returns:
+            A dictionary containing the processed conscious output
+        """
         awareness = await self.awareness_engine.process(input_state)
         integrated_state = await self.integration_manager.integrate(awareness)
         self_update = await self.self_model.update(integrated_state)
@@ -50,8 +105,18 @@ class ConsciousnessKernel:
             self_model=self_update
         )
         
-        return self._generate_conscious_output(experience)
+        # Record the state for historical tracking
+        if isinstance(integrated_state, ConsciousnessState):
+            self.state_history.append(integrated_state)
+        
+        return await self._generate_conscious_output(experience)
     def _initialize_consciousness_state(self) -> ConsciousnessState:
+        """
+        Initialize a default consciousness state with zero values.
+        
+        Returns:
+            A default ConsciousnessState object
+        """
         return ConsciousnessState(
             integration_level=0.0,
             phi_prime=0.0,
@@ -61,15 +126,69 @@ class ConsciousnessKernel:
             temporal_continuity=0.0
         )
     
-    def process_consciousness(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
-        phi_value = self.phi_prime_calculator.compute(input_state)
-        attention_state = self.attention_system.allocate(input_state)
-        meta_state = self.meta_monitor.evaluate(input_state)
+    async def process_consciousness(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Process consciousness using the traditional phi-based approach.
+        This is an alternative to process_consciousness_cycle that uses different components.
+        
+        Args:
+            input_state: The input state containing sensory and contextual information
+            
+        Returns:
+            A dictionary containing the processed conscious output
+        """
+        phi_value = await self.phi_prime_calculator.compute(input_state)
+        attention_state = await self.attention_system.allocate(input_state)
+        meta_state = await self.meta_monitor.evaluate(input_state)
         
-        phenomenological_experience = self.phenomenological_simulator.simulate(
+        phenomenological_experience = await self.phenomenological_simulator.simulate(
             phi_value,
             attention_state,
             meta_state
         )
         
-        return self._integrate_consciousness_state(phenomenological_experience)
+        return await self._integrate_consciousness_state(phenomenological_experience)
+        
+    async def _generate_conscious_output(self, experience: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Generate the final conscious output based on the simulated experience.
+        
+        Args:
+            experience: The simulated experience data
+            
+        Returns:
+            A dictionary containing the final conscious output
+        """
+        # Process the experience into a coherent output format
+        output = {
+            "content": experience.get("content", ""),
+            "emotional_tone": experience.get("emotional_tone", {}),
+            "meta_cognition": experience.get("meta_cognition", {}),
+            "phenomenal_qualities": experience.get("qualia", {}),
+            "teleological_vector": experience.get("purpose_direction", {})
+        }
+        
+        return output
+        
+    async def _integrate_consciousness_state(self, experience: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Integrate a phenomenological experience into a consciousness state.
+        
+        Args:
+            experience: The phenomenological experience to integrate
+            
+        Returns:
+            A dictionary containing the integrated consciousness state
+        """
+        # Create an integrated output based on the phenomenological experience
+        integrated_output = {
+            "integrated_state": {
+                "phi_value": experience.get("phi_value", 0.0),
+                "meta_awareness": experience.get("meta_level", {}),
+                "attention_field": experience.get("attention_distribution", {})
+            },
+            "qualia_map": experience.get("qualia", {}),
+            "response": experience.get("content", "")
+        }
+        
+        return integrated_output

src/core/integration_manager.py

@@ -0,0 +1,325 @@
+"""
+Integration Manager Module.
+
+This module contains the IntegrationManager class which is responsible for
+integrating different consciousness states and managing their interactions
+within the Hybrid Intelligence Model (HIM) system.
+"""
+
+import asyncio
+from typing import Dict, Any, Optional, List, TypeVar, Generic
+from dataclasses import dataclass
+from enum import Enum, auto
+
+# Type definitions
+T = TypeVar('T')
+
+class AwarenessLevel(Enum):
+    """Enum representing different levels of awareness."""
+    UNCONSCIOUS = auto()
+    SUBCONSCIOUS = auto()
+    CONSCIOUS = auto()
+    SELF_AWARE = auto()
+    TRANSCENDENT = auto()
+
+@dataclass
+class AwarenessState:
+    """
+    Data class representing a state of awareness.
+    
+    Attributes:
+        level (AwarenessLevel): The level of awareness.
+        perception_data (Dict[str, Any]): Data related to perceptions.
+        cognition_state (Dict[str, Any]): Current cognitive state information.
+        emotional_valence (float): Emotional valence value from -1.0 to 1.0.
+        semantic_context (Optional[Dict[str, Any]]): Optional semantic context.
+        temporal_awareness (Optional[Dict[str, Any]]): Awareness of time-related aspects.
+    """
+    level: AwarenessLevel
+    perception_data: Dict[str, Any]
+    cognition_state: Dict[str, Any]
+    emotional_valence: float  # Range from -1.0 to 1.0
+    semantic_context: Optional[Dict[str, Any]] = None
+    temporal_awareness: Optional[Dict[str, Any]] = None
+    
+@dataclass
+class IntegratedState(Generic[T]):
+    """
+    Data class representing an integrated consciousness state.
+    
+    Attributes:
+        primary_awareness (AwarenessState): The primary awareness state.
+        secondary_states (List[AwarenessState]): List of secondary awareness states.
+        integration_coherence (float): Coherence level of the integration (0.0 to 1.0).
+        emergent_properties (Dict[str, Any]): Properties emerging from integration.
+        teleological_vector (Optional[Dict[str, float]]): Direction of purposeful action.
+    """
+    primary_awareness: AwarenessState
+    secondary_states: List[AwarenessState]
+    integration_coherence: float  # Range from 0.0 to 1.0
+    emergent_properties: Dict[str, Any]
+    teleological_vector: Optional[Dict[str, float]] = None
+
+
+class IntegrationManager:
+    """
+    Manages the integration of different consciousness states and their interactions.
+    
+    This class provides methods to integrate awareness states, manage transitions
+    between states, and handle interactions between different consciousness components.
+    It serves as a core component in the consciousness architecture of HIM.
+    """
+    
+    def __init__(self, integration_threshold: float = 0.7, coherence_factor: float = 0.85):
+        """
+        Initialize the IntegrationManager.
+        
+        Args:
+            integration_threshold (float): Minimum threshold for integration to occur.
+            coherence_factor (float): Factor influencing coherence of integrated states.
+        """
+        self.integration_threshold = integration_threshold
+        self.coherence_factor = coherence_factor
+        self.state_history: List[IntegratedState] = []
+        self.integration_lock = asyncio.Lock()
+    
+    async def integrate(self, 
+                        awareness_state: AwarenessState, 
+                        secondary_states: Optional[List[AwarenessState]] = None) -> IntegratedState:
+        """
+        Integrate an awareness state with optional secondary states.
+        
+        This asynchronous method takes a primary awareness state and optional
+        secondary states, and integrates them into a coherent consciousness state.
+        The integration process considers the relationships between states,
+        their coherence, and emergent properties from their combination.
+        
+        Args:
+            awareness_state (AwarenessState): The primary awareness state to integrate.
+            secondary_states (Optional[List[AwarenessState]]): Secondary states to integrate.
+                                                              Defaults to None.
+        
+        Returns:
+            IntegratedState: A new integrated consciousness state.
+        
+        Raises:
+            ValueError: If awareness_state is invalid or integration fails.
+        """
+        if not isinstance(awareness_state, AwarenessState):
+            raise ValueError("Primary awareness state must be of type AwarenessState")
+        
+        # Use empty list if secondary_states is None
+        secondary_states = secondary_states or []
+        
+        async with self.integration_lock:
+            # Calculate coherence based on state compatibility
+            coherence = self._calculate_coherence(awareness_state, secondary_states)
+            
+            # Generate emergent properties through integration
+            emergent_properties = await self._generate_emergent_properties(
+                awareness_state, 
+                secondary_states,
+                coherence
+            )
+            
+            # Calculate teleological vector (purposeful direction)
+            teleological_vector = self._calculate_teleological_vector(
+                awareness_state,
+                secondary_states
+            )
+            
+            # Create the integrated state
+            integrated_state = IntegratedState(
+                primary_awareness=awareness_state,
+                secondary_states=secondary_states,
+                integration_coherence=coherence,
+                emergent_properties=emergent_properties,
+                teleological_vector=teleological_vector
+            )
+            
+            # Add to history and return
+            self.state_history.append(integrated_state)
+            return integrated_state
+    
+    def _calculate_coherence(self, 
+                           primary: AwarenessState, 
+                           secondaries: List[AwarenessState]) -> float:
+        """
+        Calculate the coherence between the primary and secondary states.
+        
+        Args:
+            primary (AwarenessState): Primary awareness state.
+            secondaries (List[AwarenessState]): List of secondary awareness states.
+        
+        Returns:
+            float: Coherence value between 0.0 and 1.0.
+        """
+        # Simplified coherence calculation
+        if not secondaries:
+            return 1.0  # Perfect coherence with only primary state
+        
+        # Base coherence starts at coherence_factor and is modified by state compatibility
+        base_coherence = self.coherence_factor
+        
+        # Factor in emotional alignment
+        emotional_alignment = sum(
+            1 - abs(primary.emotional_valence - secondary.emotional_valence) / 2
+            for secondary in secondaries
+        ) / len(secondaries)
+        
+        # Factor in awareness level compatibility
+        level_compatibility = sum(
+            1 - abs(primary.level.value - secondary.level.value) / 5  # Normalize by max enum difference
+            for secondary in secondaries
+        ) / len(secondaries)
+        
+        # Weighted combination
+        coherence = (base_coherence * 0.5 + 
+                    emotional_alignment * 0.3 + 
+                    level_compatibility * 0.2)
+        
+        return max(0.0, min(1.0, coherence))  # Clamp between 0 and 1
+    
+    async def _generate_emergent_properties(self,
+                                         primary: AwarenessState,
+                                         secondaries: List[AwarenessState],
+                                         coherence: float) -> Dict[str, Any]:
+        """
+        Generate emergent properties from the integration of awareness states.
+        
+        Args:
+            primary (AwarenessState): Primary awareness state.
+            secondaries (List[AwarenessState]): List of secondary awareness states.
+            coherence (float): Calculated coherence of the integration.
+        
+        Returns:
+            Dict[str, Any]: Dictionary of emergent properties.
+        """
+        emergent_properties = {
+            "coherence_level": coherence,
+            "awareness_depth": self._calculate_awareness_depth(primary, secondaries),
+            "cognitive_complexity": self._calculate_cognitive_complexity(primary, secondaries)
+        }
+        
+        # Simulate computational intensity with sleep
+        await asyncio.sleep(0.01)
+        
+        # Add semantic richness if semantic contexts are available
+        if primary.semantic_context:
+            emergent_properties["semantic_richness"] = len(primary.semantic_context)
+            
+            if any(s.semantic_context for s in secondaries if s.semantic_context):
+                emergent_properties["semantic_richness"] += sum(
+                    len(s.semantic_context or {}) for s in secondaries
+                ) / (len(secondaries) + 1)  # Average including primary
+        
+        return emergent_properties
+    
+    def _calculate_awareness_depth(self, 
+                                primary: AwarenessState, 
+                                secondaries: List[AwarenessState]) -> float:
+        """
+        Calculate the depth of awareness from the states.
+        
+        Args:
+            primary (AwarenessState): Primary awareness state.
+            secondaries (List[AwarenessState]): List of secondary awareness states.
+        
+        Returns:
+            float: Calculated awareness depth value.
+        """
+        # Base depth from primary state's level
+        base_depth = primary.level.value / len(AwarenessLevel)
+        
+        # Enhance with secondary states if present
+        if secondaries:
+            secondary_contribution = sum(s.level.value for s in secondaries) / (len(secondaries) * len(AwarenessLevel))
+            # Weighted combination
+            return (base_depth * 0.7) + (secondary_contribution * 0.3)
+        
+        return base_depth
+    
+    def _calculate_cognitive_complexity(self, 
+                                     primary: AwarenessState, 
+                                     secondaries: List[AwarenessState]) -> float:
+        """
+        Calculate the cognitive complexity of the integrated state.
+        
+        Args:
+            primary (AwarenessState): Primary awareness state.
+            secondaries (List[AwarenessState]): List of secondary awareness states.
+        
+        Returns:
+            float: Cognitive complexity value.
+        """
+        # Base complexity from primary state
+        base_complexity = len(primary.cognition_state) / 10  # Normalize
+        
+        # Enhance with secondary states
+        if secondaries:
+            # Average complexity of secondaries
+            secondary_complexity = sum(len(s.cognition_state) for s in secondaries) / len(secondaries) / 10
+            interaction_factor = len(secondaries) * 0.1  # More states = more complexity
+            
+            return min(1.0, base_complexity + secondary_complexity + interaction_factor)
+        
+        return min(1.0, base_complexity)
+    
+    def _calculate_teleological_vector(self,
+                                    primary: AwarenessState,
+                                    secondaries: List[AwarenessState]) -> Dict[str, float]:
+        """
+        Calculate the teleological vector representing purposeful direction.
+        
+        Args:
+            primary (AwarenessState): Primary awareness state.
+            secondaries (List[AwarenessState]): List of secondary awareness states.
+        
+        Returns:
+            Dict[str, float]: A vector of purpose directions and intensities.
+        """
+        # Define basic teleological dimensions
+        teleological_vector = {
+            "meaning_seeking": 0.5,
+            "self_preservation": 0.5,
+            "complexity_increase": 0.5,
+            "coherence_maintenance": 0.5,
+            "purposeful_action": 0.5
+        }
+        
+        # Modify based on primary state
+        if primary.level == AwarenessLevel.SELF_AWARE or primary.level == AwarenessLevel.TRANSCENDENT:
+            teleological_vector["meaning_seeking"] += 0.2
+            teleological_vector["complexity_increase"] += 0.1
+        
+        # Emotional valence affects self-preservation and purposeful action
+        teleological_vector["self_preservation"] += primary.emotional_valence * 0.2
+        teleological_vector["purposeful_action"] += abs(primary.emotional_valence) * 0.3
+        
+        # Secondary states influence
+        if secondaries:
+            # Coherence maintenance influenced by number of states to integrate
+            teleological_vector["coherence_maintenance"] += min(0.4, len(secondaries) * 0.1)
+            
+            # Average emotional valence affects meaning seeking
+            avg_emotion = sum(s.emotional_valence for s in secondaries) / len(secondaries)
+            teleological_vector["meaning_seeking"] += avg_emotion * 0.1
+        
+        # Normalize values to 0.0-1.0 range
+        for key in teleological_vector:
+            teleological_vector[key] = max(0.0, min(1.0, teleological_vector[key]))
+            
+        return teleological_vector
+    
+    def get_integration_history(self, limit: int = 10) -> List[IntegratedState]:
+        """
+        Retrieve recent integration history.
+        
+        Args:
+            limit (int): Maximum number of history items to return. Defaults to 10.
+        
+        Returns:
+            List[IntegratedState]: Recent integration states.
+        """
+        return self.state_history[-limit:] if self.state_history else []
+