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| """ | |
| 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() | |
| 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 | |
| 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 [] | |