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from dataclasses import dataclass
from enum import Enum
import torch
import numpy as np
from typing import Dict, List, Optional
@dataclass
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"
from typing import Dict, List, Any
import asyncio
import torch
class ConsciousnessKernel:
def __init__(self):
self.awareness_engine = AwarenessEngine()
self.integration_manager = IntegrationManager()
self.self_model = DynamicSelfModel()
self.experience_simulator = ExperienceSimulator()
async def process_consciousness_cycle(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
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
)
return self._generate_conscious_output(experience)
def _initialize_consciousness_state(self) -> ConsciousnessState:
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
)
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)
phenomenological_experience = self.phenomenological_simulator.simulate(
phi_value,
attention_state,
meta_state
)
return self._integrate_consciousness_state(phenomenological_experience) |