Spaces:
Sleeping
Sleeping
File size: 13,583 Bytes
3c5e81f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 |
"""
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 []
|