Upload quantum_cognition_engine.py

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