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| from dataclasses import dataclass | |
| from enum import Enum | |
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| 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 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} | |
| } | |
| class ConsciousnessState: | |
| integration_level: float | |
| phi_prime: float | |
| awareness_vector: np.ndarray | |
| emotional_state: np.ndarray | |
| attention_focus: Dict[str, float] | |
| temporal_continuity: float | |
| class ConsciousnessLevel(Enum): | |
| PROTO = "proto_consciousness" | |
| FUNCTIONAL = "functional_consciousness" | |
| REFLECTIVE = "reflective_consciousness" | |
| INTEGRATED = "integrated_consciousness" | |
| 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) | |
| # 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) | |
| experience = await self.experience_simulator.simulate( | |
| awareness=awareness, | |
| integrated_state=integrated_state, | |
| self_model=self_update | |
| ) | |
| # 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, | |
| awareness_vector=np.zeros(self.awareness_dimension), | |
| emotional_state=np.zeros(self.emotional_dimension), | |
| attention_focus={}, | |
| temporal_continuity=0.0 | |
| ) | |
| 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 = await self.phenomenological_simulator.simulate( | |
| phi_value, | |
| attention_state, | |
| meta_state | |
| ) | |
| 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 | |