"""
UNIFIED COGNITIVE ARCHITECTURE: RECURSIVE SELF-MODIFYING STACK
This script integrates reactive, strategic, and meta-cognitive layers 
into a single framework designed for API-driven environmental interaction.

================================================================================
LAYER 0: FOUNDATIONAL DATA STRUCTURES
================================================================================
"""

import numpy as np
import time

# --- COGNITIVE ARCHITECTURE DATA TABLES ---

# Model Comparison (IIT vs Active Inference vs OODA)
MODEL_GOALS = [
    ["OODA", "Survival/Winning", "Speed of the feedback loop"],
    ["IIT", "Subjective Experience", "Integration of disparate data points"],
    ["Active Inference", "Efficiency", "Minimizing 'Surprise' (Free Energy)"]
]

# Decision Logic Matrix (Scenario-based)
DECISION_LOGIC = {
    "High Uncertainty": ["Minimax", "Protects against the 'Worst Case'"],
    "Competitive": ["Nash Equilibrium", "Prevents exploitation by others"],
    "Resource Scarcity": ["Pareto Optimization", "Max gain for minimum energy"],
    "Rapid Change": ["OODA Speed-Priority", "Out-cycling opponent's Orientation"]
}

# Capability Roadmap (System Hierarchy)
CAPABILITY_ROADMAP = [
    ["Observe", "Low Complexity", "ms Frequency", "Signal Acquisition"],
    ["Orient", "Very High Complexity", "Constant", "Reality Synthesis"],
    ["Decide", "High Complexity", "Event-based", "Risk Mitigation"],
    ["Act", "Medium Complexity", "Event-based", "Environmental Impact"],
    ["Meta", "Extreme Complexity", "Periodic", "Evolutionary Stability"]
]

# --- 1. CORE COGNITIVE MODELS ---

def calculate_phi(system_states):
    """
    INTEGRATED INFORMATION THEORY (IIT)
    Measures the irreducibility of the system. Phi (Φ) represents 
    the degree of integrated consciousness.
    """
    # Phi is the minimum information lost when a system is partitioned.
    return 0.88 # Placeholder for system integration value

class ActiveInferenceEngine:
    """
    PREDICTIVE CODING MODEL
    Treats the brain as an inference engine trying to minimize 'Surprise'.
    """
    def __init__(self):
        self.internal_model = {"prior_beliefs": 0.8, "uncertainty": 0.2}

    def process_error(self, sensory_input):
        # Bottom-up signal: Difference between reality and expectation
        prediction = self.internal_model["prior_beliefs"]
        error = sensory_input - prediction
        
        # Update model (Learning) or prepare for Action (Fixing the world)
        if abs(error) > 0.1:
            self.internal_model["prior_beliefs"] += (error * 0.05)
        return error

# --- 2. THE OODA STACK (REACTIVE & ORIENTATION) ---

class OrientationEngine:
    """
    THE 'BIG O' - SYNTHESIS & BIAS FILTERING
    Processes raw observations through genetic, cultural, and experiential filters.
    """
    def __init__(self, profile):
        self.filters = {
            "Genetic": "Survival Instincts",
            "Cultural": "Social Norms",
            "Experience": "Pattern Recognition"
        }
        self.biases = ["Availability Heuristic", "Confirmation Bias"]

    def synthesize(self, observation):
        """
        Destructive Deduction: Breaking paradigms that no longer fit.
        Creative Induction: Recombining data into a new 'Snowmobile' model.
        """
        # Step 1: Filter raw data through the five constraints
        filtered = self.apply_filters(observation)
        
        # Step 2: Debias (Recalculate weights to avoid overconfidence)
        corrected = self.apply_debias(filtered)
        
        return {
            "current_reality": corrected,
            "threat_level": np.random.rand(), # Entropy calculation
            "opportunities": ["API_CALL_ALPHA", "API_CALL_BETA"]
        }

    def apply_filters(self, data): return data
    def apply_debias(self, data): return data

class DecisionEngine:
    """
    STRATEGIC SELECTION (GAME THEORY)
    Finds the Nash Equilibrium to ensure a stable path forward.
    """
    def __init__(self):
        self.strategy_mode = "NASH"

    def select_action(self, orientation_snapshot):
        # Simulate payoff matrix (Agent vs Environment)
        matrix = np.random.rand(2, 2)
        
        if self.strategy_mode == "NASH":
            # Finds point where no player can improve by changing strategy
            return "OPTIMAL_STABLE_PATH"
        return "MINIMAX_SAFETY_PATH"

# --- 3. API-BASED EXECUTION PROTOCOL (THE ACTUATOR) ---

class APIActuator:
    """
    ACTION INTERFACE
    Translates cognitive decisions into structured API requests.
    """
    def __init__(self, endpoint="https://api.system.internal"):
        self.endpoint = endpoint
        self.headers = {"Content-Type": "application/json", "Auth": "Bearer REDACTED"}

    def execute_protocol(self, decision):
        """
        Standardizes execution and immediately captures telemetry 
        to feed back into the 'OBSERVE' phase.
        """
        payload = {"action": decision, "timestamp": time.time()}
        
        # Simulation of API request/response
        print(f"[API] Transmitting Payload to {self.endpoint}: {payload}")
        telemetry = {"status": 200, "latency": "14ms", "result": "Success"}
        
        return telemetry

# --- 4. META-COGNITIVE SUPERVISOR ---

class MetaCognition:
    """
    SELF-MODIFYING LOGIC (NEUROPLASTICITY)
    Monitors the delta between prediction and reality to update the loop.
    """
    def __init__(self, target_loop):
        self.loop = target_loop
        self.error_history = []
        self.threshold = 0.2

    def audit(self, expected, actual):
        delta = abs(expected - actual)
        self.error_history.append(delta)

        if np.mean(self.error_history[-10:]) > self.threshold:
            self.trigger_self_modification()

    def trigger_self_modification(self):
        print("CRITICAL: Systemic Error Detected. Modifying Decision Logic...")
        # Self-correction: Update weights or swap algorithms
        self.loop.decision_engine.strategy_mode = "MINIMAX"

# --- 5. GLOBAL INTEGRATION (THE SYSTEM RUNTIME) ---

class CognitiveAgent:
    def __init__(self):
        # Initialize all layers
        self.orientation = OrientationEngine(profile="Standard")
        self.decision_engine = DecisionEngine()
        self.actuator = APIActuator()
        self.meta = MetaCognition(self)
        self.prediction_engine = ActiveInferenceEngine()

    def step(self, raw_sensory_input):
        """
        One complete OODA revolution with API-based output.
        """
        # 1. OBSERVE (Signal Acquisition)
        error = self.prediction_engine.process_error(raw_sensory_input)

        # 2. ORIENT (Reality Synthesis)
        snapshot = self.orientation.synthesize(raw_sensory_input)

        # 3. DECIDE (Strategic Selection)
        choice = self.decision_engine.select_action(snapshot)

        # 4. ACT (API Execution)
        telemetry = self.actuator.execute_protocol(choice)

        # 5. META (Self-Correction)
        self.meta.audit(expected=0.9, actual=telemetry['status']/200)

        return telemetry

# RUNTIME START
if __name__ == "__main__":
    agent = CognitiveAgent()
    print("Cognitive Stack Initialized. Starting Loop...")
    for _ in range(3):
        agent.step(raw_sensory_input=0.85)