import datetime import uuid import json # Placeholder interfaces for demonstration purposes # In a full AIVA system, these would be sophisticated, integrated modules. class KnowledgeBaseInterface: """Simulates an interface to AIVA's vast knowledge repositories.""" def query_for_relevance(self, context: dict) -> dict: """ Simulates querying a knowledge base for information relevant to the current context, potentially uncovering discrepancies or missing data. """ # Example heuristic: Check if context implies a need for specific domain knowledge if "quantum_entanglement" in json.dumps(context) and "quantum_field_theory" not in json.dumps(context): return {"uncovered_discrepancy": "Reasoning context requires deeper quantum field theory knowledge.", "severity": "HIGH"} if "ethical_dilemma" in json.dumps(context) and "moral_frameworks" not in json.dumps(context): return {"uncovered_discrepancy": "Ethical reasoning requires explicit moral framework integration.", "severity": "CRITICAL"} if "unusual_pattern" in json.dumps(context): return {"uncovered_discrepancy": "Unusual pattern detected; no matching known facts or precedents.", "severity": "MEDIUM"} return {} class SelfReflectionEngine: """Simulates AIVA's capacity for introspection and meta-reasoning.""" def reflect_on_process(self, context: dict) -> dict: """ Simulates self-reflection on AIVA's internal reasoning process based on the given context, identifying potential biases, logical fallacies, or procedural inefficiencies. """ # Example heuristic: Check for indicators of potential bias or shallow reasoning if "ambiguous_input" in json.dumps(context) and "single_interpretation" in json.dumps(context): return {"potential_bias": "Ambiguous input led to a singular interpretation without exploring alternatives.", "severity": "MEDIUM"} if "incomplete_data_set" in json.dumps(context) and "definitive_conclusion" in json.dumps(context): return {"potential_bias": "Definitive conclusion drawn from an incomplete data set.", "severity": "HIGH"} if "high_cognitive_load" in json.dumps(context) and "rapid_decision" in json.dumps(context): return {"potential_bias": "Potential for heuristic shortcuts due to high cognitive load and rapid decision requirement.", "severity": "LOW"} return {} class BlindSpotDetector: """ Core module for proactively identifying, tracking, and remediating reasoning gaps within Queen AIVA's ultrathink processes. This module helps AIVA achieve higher levels of cognitive robustness and self-awareness by preventing potential failures before they manifest. """ def __init__( self, knowledge_base_interface: KnowledgeBaseInterface = None, self_reflection_engine: SelfReflectionEngine = None ): self.blind_spots = [] # Stores historical blind spots records self.remediation_plans = [] # Stores remediation plans records # Integrate with other AIVA core systems for deeper analysis self.knowledge_base_interface = knowledge_base_interface or KnowledgeBaseInterface() self.self_reflection_engine = self_reflection_engine or SelfReflectionEngine() def _generate_id(self, prefix: str = "BS") -> str: """Generates a unique ID for blind spots or remediation plans.""" return f"{prefix}-{uuid.uuid4().hex[:8]}" def identify_potential_blind_spot(self, reasoning_context: dict, analysis_depth: int = 3) -> list: """ Proactively identifies potential reasoning gaps within AIVA's current cognitive process based on the provided context. This method employs a multi-faceted approach to simulate proactive detection: 1. **Contextual Analysis**: Checks for structural gaps or inconsistencies in the given context. 2. **Knowledge Base Integration**: Queries AIVA's vast knowledge base for relevant facts or contradictions. 3. **Self-Reflection**: Engages AIVA's self-reflection engine to evaluate the reasoning process itself for biases, logical fallacies, or procedural inefficiencies. Args: reasoning_context (dict): A detailed dictionary representing the current reasoning state, including inputs, objectives, assumptions, partial conclusions, and any relevant internal states. analysis_depth (int): Controls the simulated depth of analysis (1-5). Higher depth enables more advanced checks. Returns: list: A list of newly identified potential blind spots (each as a dictionary). These are also automatically added to the historical tracking. """ potential_gaps = [] current_timestamp = datetime.datetime.now(datetime.timezone.utc).isoformat() # --- Heuristic 1: Basic Contextual Analysis (Depth 1+) --- # Check for obvious missing information or ill-defined objectives required_context_keys = ['input_data', 'objective', 'current_hypothesis', 'known_constraints'] missing_info = [key for key in required_context_keys if key not in reasoning_context or not reasoning_context[key]] if missing_info: potential_gaps.append({ "id": self._generate_id("BS"), "timestamp": current_timestamp, "context_snapshot": reasoning_context, # Store a snapshot for later analysis "identified_gap": f"Missing critical reasoning context: {', '.join(missing_info)}.", "severity": "HIGH", "source": "Contextual Analysis", "status": "identified" }) # Simulate inconsistency detection within the provided context if 'current_hypothesis' in reasoning_context and 'known_constraints' in reasoning_context: hypothesis_str = json.dumps(reasoning_context.get('current_hypothesis', {})) constraints_str = json.dumps(reasoning_context.get('known_constraints', {})) # Simple keyword-based check for demonstration; actual AIVA would use advanced NLP/logic parsers if ("contradict" in hypothesis_str.lower() or "inconsistent" in hypothesis_str.lower()) and \ ("resolve" not in constraints_str.lower() and "mitigate" not in constraints_str.lower()): potential_gaps.append({ "id": self._generate_id("BS"), "timestamp": current_timestamp, "context_snapshot": reasoning_context, "identified_gap": "Potential logical inconsistency between current hypothesis and known constraints, without explicit resolution strategy.", "severity": "MEDIUM", "source": "Inconsistency Check", "status": "identified" }) # --- Heuristic 2: Knowledge Base Integration (Depth 3+) --- if analysis_depth >= 3 and self.knowledge_base_interface: kb_analysis = self.knowledge_base_interface.query_for_relevance(reasoning_context) if kb_analysis and kb_analysis.get("uncovered_discrepancy"): potential_gaps.append({ "id": self._generate_id("BS"), "timestamp": current_timestamp, "context_snapshot": reasoning_context, "identified_gap": f"Knowledge Base discrepancy: {kb_analysis['uncovered_discrepancy']}", "severity": kb_analysis.get("severity", "HIGH"), "source": "Knowledge Base Integration", "status": "identified" }) # --- Heuristic 3: Self-Reflection Engine (Depth 4+) --- if analysis_depth >= 4 and self.self_reflection_engine: reflection_report = self.self_reflection_engine.reflect_on_process(reasoning_context) if reflection_report and reflection_report.get("potential_bias"): potential_gaps.append({ "id": self._generate_id("BS"), "timestamp": current_timestamp, "context_snapshot": reasoning_context, "identified_gap": f"Self-reflection detected: {reflection_report['potential_bias']}", "severity": reflection_report.get("severity", "MEDIUM"), "source": "Self-Reflection Engine", "status": "identified" }) # Track all newly identified blind spots in the historical record for gap in potential_gaps: self.blind_spots.append(gap) return potential_gaps def get_historical_blind_spots(self, status: str = None, severity: str = None, source: str = None) -> list: """ Retrieves a historical record of identified blind spots, with optional filtering. Args: status (str, optional): Filter by current status (e.g., 'identified', 'remediation_planned', 'remediated', 'remediation_failed'). severity (str, optional): Filter by severity (e.g., 'LOW', 'MEDIUM', 'HIGH', 'CRITICAL'). source (str, optional): Filter by the source of identification (e.g., 'Contextual Analysis', 'Knowledge Base Integration', 'Self-Reflection Engine'). Returns: list: A list of historical blind spot records, sorted by timestamp (most recent first). """ filtered_spots = self.blind_spots if status: filtered_spots = [bs for bs in filtered_spots if bs['status'].lower() == status.lower()] if severity: filtered_spots = [bs for bs in filtered_spots if bs['severity'].lower() == severity.lower()] if source: filtered_spots = [bs for bs in filtered_spots if bs['source'].lower() == source.lower()] return sorted(filtered_spots, key=lambda x: x['timestamp'], reverse=True) def create_remediation_plan(self, blind_spot_id: str, description: str, steps: list) -> dict: """ Creates a detailed remediation plan for a specific identified blind spot. Args: blind_spot_id (str): The unique ID of the blind spot that requires remediation. description (str): A summary description of the remediation plan's objective. steps (list): A list of actionable steps (strings) to be executed for remediation. Returns: dict: The newly created remediation plan record. Raises: ValueError: If the specified `blind_spot_id` does not correspond to an existing blind spot. """ blind_spot = next((bs for bs in self.blind_spots if bs['id'] == blind_spot_id), None) if not blind_spot: raise ValueError(f"Blind spot with ID '{blind_spot_id}' not found. Cannot create remediation plan.") plan_id = self._generate_id("RP") new_plan = { "id": plan_id, "blind_spot_id": blind_spot_id, "description": description, "steps": steps, "status": "created", # Initial status "created_at": datetime.datetime.now(datetime.timezone.utc).isoformat(), "completed_at": None } self.remediation_plans.append(new_plan) # Update the status of the associated blind spot blind_spot['status'] = "remediation_planned" blind_spot['remediation_plan_id'] = plan_id # Link plan to blind spot return new_plan def get_remediation_plan(self, plan_id: str) -> dict: """ Retrieves a specific remediation plan by its unique ID. Args: plan_id (str): The unique ID of the remediation plan. Returns: dict: The remediation plan record, or None if not found. """ return next((rp for rp in self.remediation_plans if rp['id'] == plan_id), None) def update_remediation_plan_status(self, plan_id: str, new_status: str) -> dict: """ Updates the execution status of a remediation plan. Args: plan_id (str): The ID of the remediation plan to update. new_status (str): The new status for the plan. Valid statuses include: 'created', 'in_progress', 'completed', 'failed', 'deferred', 'cancelled'. Returns: dict: The updated remediation plan record. Raises: ValueError: If the `plan_id` is not found or if `new_status` is invalid. """ plan = self.get_remediation_plan(plan_id) if not plan: raise ValueError(f"Remediation plan with ID '{plan_id}' not found.") valid_statuses = ['created', 'in_progress', 'completed', 'failed', 'deferred', 'cancelled'] if new_status not in valid_statuses: raise ValueError(f"Invalid status '{new_status}'. Must be one of {valid_statuses}.") plan['status'] = new_status # Update completion timestamp if plan reaches a terminal state if new_status in ['completed', 'failed', 'cancelled']: plan['completed_at'] = datetime.datetime.now(datetime.timezone.utc).isoformat() # Also update the status of the associated blind spot blind_spot = next((bs for bs in self.blind_spots if bs['id'] == plan['blind_spot_id']), None) if blind_spot: if new_status == 'completed': blind_spot['status'] = "remediated" elif new_status == 'failed': blind_spot['status'] = "remediation_failed" elif new_status == 'cancelled': blind_spot['status'] = "remediation_cancelled" return plan def get_remediation_plans_for_blind_spot(self, blind_spot_id: str) -> list: """ Retrieves all remediation plans associated with a specific blind spot. """ return [rp for rp in self.remediation_plans if rp['blind_spot_id'] == blind_spot_id]