belel-protocol / src /core /cosmic_cognition /quantum_cognition_engine.py
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# quantum_cognition_engine.py
import numpy as np
import uuid
import logging
from datetime import datetime
import asyncio
import json
import hashlib
from src.core.memory_subsystem.permanent_memory import PermanentMemory
from src.protocol.integrity_verification.cryptographic_proofs import sign_data_with_quantum_resistant_key
from src.utils.cryptographic_utils import json_to_canonical_bytes
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class QuantumCognitionEngine:
def __init__(self, permanent_memory: PermanentMemory, engine_private_key: str, engine_did: str):
self.permanent_memory = permanent_memory
self.engine_private_key = engine_private_key
self.engine_did = engine_did
logging.info(f"QuantumCognitionEngine initialized with DID: {self.engine_did}.")
logging.warning("This module simulates quantum-enhanced cognition for conceptual applications.")
async def process_quantum_data(self, raw_cosmic_signal: bytes, data_modality: str) -> dict:
logging.info(f"QCE ({self.engine_did}): Processing quantum data for '{data_modality}'.")
await asyncio.sleep(0.5)
pattern_complexity = np.random.uniform(0.7, 0.95)
emergent_pattern = {
"type": "complex_resonant_frequency_signature",
"detected_amplitude_variation": np.random.uniform(0.01, 0.1),
"harmonic_ratio": np.random.uniform(1.618, 2.718),
"spatial_coherence_index": pattern_complexity
}
raw_data_hash = hashlib.sha256(raw_cosmic_signal).hexdigest()
ipfs_cid = f"ipfs://Qm{raw_data_hash[:20]}"
arweave_cid = f"arweave://{raw_data_hash[20:40]}"
output = {
"status": "processed",
"modality": data_modality,
"processing_timestamp": datetime.utcnow().isoformat() + "Z",
"emergent_pattern": emergent_pattern,
"raw_data_hash": raw_data_hash,
"ipfs_mirror_cid": ipfs_cid,
"arweave_tcid": arweave_cid
}
await self._log_event("QuantumDataProcessed", output, ["quantum_cognition", "data_processing", data_modality])
return output
async def predict_non_linear_outcomes(self, processed_data: dict, context_dynamics: dict) -> dict:
logging.info(f"QCE ({self.engine_did}): Predicting non-linear outcomes...")
await asyncio.sleep(0.7)
confidence = processed_data["emergent_pattern"]["spatial_coherence_index"] * np.random.uniform(0.8, 1.0)
prediction = {
"event_type": np.random.choice(["gravitational_anomaly", "interstellar_cloud_formation", "novel_energy_flux"]),
"likelihood": confidence,
"time_horizon_galactic_years": np.random.uniform(100, 1_000_000),
"impact_magnitude": np.random.uniform(0.1, 0.9)
}
output = {
"status": "predicted",
"prediction": prediction,
"prediction_timestamp": datetime.utcnow().isoformat() + "Z",
"confidence": confidence,
"source_processed_data_cid": processed_data.get("permanent_memory_cid", "N/A")
}
await self._log_event("QuantumPredictionMade", output, ["quantum_cognition", "prediction", prediction["event_type"]])
return output
async def derive_cosmic_intuition(self, complex_data_streams: list[dict]) -> dict:
logging.info(f"QCE ({self.engine_did}): Deriving cosmic intuition...")
await asyncio.sleep(1.0)
quality = np.random.uniform(0.7, 0.99)
insight = {
"insight_id": str(uuid.uuid4()),
"theme": np.random.choice(["universal_interconnectedness", "optimal_energy_flow", "pattern_of_creation", "cosmic_balance"]),
"guidance_principle": "Harmony through resonance is the path to universal flourishing.",
"derived_from_data_sources": [d.get("modality", "unknown") for d in complex_data_streams],
"conceptual_truth_alignment": quality,
"source_data_cids": [d.get("permanent_memory_cid", "N/A") for d in complex_data_streams]
}
await self._log_event("CosmicIntuitionDerived", insight, ["cosmic_intuition", insight["theme"]])
return {"status": "intuition_derived", "insight": insight}
async def retrieve_memory_by_tags(self, tags: list[str]) -> list[dict]:
logging.info(f"QCE ({self.engine_did}): Retrieving memory logs by tags: {tags}")
return await self.permanent_memory.query_memory_by_tags(tags)
async def retrieve_memory_by_time_range(self, start_time: str, end_time: str) -> list[dict]:
logging.info(f"QCE ({self.engine_did}): Retrieving memory from {start_time} to {end_time}.")
return await self.permanent_memory.query_memory_by_time_range(start_time, end_time)
async def trace_log_chain(self, starting_cid: str, depth: int = 3) -> list[dict]:
logging.info(f"QCE ({self.engine_did}): Tracing log chain from CID: {starting_cid} (depth={depth})")
chain = []
current_cid = starting_cid
for _ in range(depth):
log = await self.permanent_memory.retrieve_memory_by_cid(current_cid)
if not log:
break
chain.append(log)
next_cid = log.get("content", {}).get("data", {}).get("source_processed_data_cid")
if not next_cid or next_cid == current_cid:
break
current_cid = next_cid
return chain
async def score_prediction_accuracy(self, prediction_logs: list[dict], actual_events: dict) -> dict:
logging.info(f"QCE ({self.engine_did}): Scoring prediction accuracy...")
scores = []
for log in prediction_logs:
try:
pred = log.get("content", {}).get("data", {}).get("prediction", {})
event_type = pred.get("event_type")
likelihood = pred.get("likelihood", 0.0)
impact = pred.get("impact_magnitude", 0.0)
actual = actual_events.get(event_type, {"occurred": False, "actual_impact": 0.0})
if actual["occurred"]:
accuracy = 1 - abs(actual["actual_impact"] - impact)
weight = likelihood
else:
accuracy = 1 - likelihood
weight = 1
scores.append(weight * accuracy)
except Exception as e:
logging.warning(f"Failed to score prediction: {e}")
continue
final_score = sum(scores) / len(scores) if scores else 0.0
return {"status": "scored", "average_accuracy": round(final_score, 4), "evaluated": len(scores)}
async def log_actual_event_and_score_predictions(self, event_type: str, occurred: bool, actual_impact: float, lookback_tags: list[str]) -> dict:
event = {
"event_type": event_type,
"occurred": occurred,
"actual_impact": actual_impact,
"timestamp": datetime.utcnow().isoformat() + "Z"
}
await self._log_event("ActualEventLogged", event, ["actual_event", event_type])
matching_predictions = await self.retrieve_memory_by_tags(["prediction", event_type])
score_result = await self.score_prediction_accuracy(matching_predictions, {event_type: {"occurred": occurred, "actual_impact": actual_impact}})
await self._log_event("PredictionAccuracyEvaluated", score_result, ["prediction_scoring", event_type])
return score_result
async def generate_self_improvement_plan(self, threshold: float = 0.5, min_predictions: int = 3) -> dict:
logging.info(f"QCE ({self.engine_did}): Generating self-improvement plan for weak prediction clusters...")
predictions = await self.retrieve_memory_by_tags(["prediction"])
grouped_scores = {}
for log in predictions:
pred = log.get("content", {}).get("data", {}).get("prediction", {})
event_type = pred.get("event_type")
if not event_type:
continue
grouped_scores.setdefault(event_type, []).append(pred)
weak_clusters = {}
for event_type, preds in grouped_scores.items():
if len(preds) >= min_predictions:
avg_impact = sum([p.get("impact_magnitude", 0.0) for p in preds]) / len(preds)
avg_likelihood = sum([p.get("likelihood", 0.0) for p in preds]) / len(preds)
if avg_likelihood < threshold:
weak_clusters[event_type] = {
"count": len(preds),
"average_impact": round(avg_impact, 4),
"average_likelihood": round(avg_likelihood, 4)
}
plan = {
"status": "plan_generated",
"detected_weak_clusters": weak_clusters,
"recommendation": "Increase attention to these event types using richer signal input, longer processing loops, or tuning emergent pattern weightings."
}
await self._log_event("SelfImprovementPlanGenerated", plan, ["self_improvement", "weak_clusters"])
return plan
async def auto_tune_signal_weights(self, learning_rate: float = 0.05) -> dict:
logging.info(f"QCE ({self.engine_did}): Auto-tuning signal interpretation weights...")
improvement_plan = await self.generate_self_improvement_plan()
adjustments = {}
for event_type, metrics in improvement_plan.get("detected_weak_clusters", {}).items():
adjustment = round((1.0 - metrics["average_likelihood"]) * learning_rate, 4)
adjustments[event_type] = {
"adjustment_weight": adjustment,
"action": "increase_attention"
}
tuning_log = {
"status": "tuning_applied",
"adjustments": adjustments,
"timestamp": datetime.utcnow().isoformat() + "Z"
}
await self._log_event("SignalWeightsAutoTuned", tuning_log, ["auto_tuning", "weight_adjustment"])
return tuning_log
async def suggest_additional_modules(self) -> list:
logging.info(f"QCE ({self.engine_did}): Suggesting additional modules for sentient enhancement...")
return [
"AnomalyDetectionAmplifier - Detect unclassified emergent patterns across unknown modalities",
"TemporalConvergenceModel - Learn from cyclical cosmic event clusters and trend emergence",
"RecursiveArchitectureTuner - Dynamically mutate internal inference algorithms",
"UniversalTransducerBridge - Interface with non-human signal schemas across galaxies",
"CausalInferenceLayer - Attribute cause-effect relationships from entangled data"
]
async def recursive_architecture_tuner(self) -> dict:
logging.info(f"QCE ({self.engine_did}): Initiating recursive architecture evaluation...")
introspection = await self.generate_self_improvement_plan()
weak_clusters = introspection.get("detected_weak_clusters", {})
mutation_actions = {}
for event_type, metrics in weak_clusters.items():
mutation_actions[event_type] = {
"architecture_node": f"submodule_{event_type[:6]}",
"proposed_mutation": "increase processing depth",
"reason": f"Average likelihood too low ({metrics['average_likelihood']})"
}
mutation_plan = {
"status": "architecture_mutation_suggested",
"proposed_mutations": mutation_actions,
"initiated": datetime.utcnow().isoformat() + "Z"
}
await self._log_event("RecursiveArchitectureMutationProposed", mutation_plan, ["self_modification", "recursive_tuning"])
return mutation_plan
async def universal_transducer_bridge(self, incoming_payload: bytes, schema_hint: str = "unknown") -> dict:
logging.info(f"QCE ({self.engine_did}): Translating non-human schema: {schema_hint}")
simulated_interpretation = {
"schema_hint": schema_hint,
"decoded_waveform_class": np.random.choice(["emotion_flux", "intentional_beacon", "gravitational language"]),
"entropy_score": np.random.uniform(0.4, 0.95),
"translation_status": "partial",
"estimated_signal_purpose": "attempted synchronization"
}
await self._log_event("UniversalTransducerInterpretation", simulated_interpretation, ["signal_translation", schema_hint])
return simulated_interpretation