""" 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: """Class for managing integration of awareness states.""" 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), "consciousness_state": str(primary.level) } # 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 []