```python import datetime import random # For simulating surprise events import numpy as np # For numerical operations and drift detection class AIVAContinuousLearningSystem: """ AIVA's continuous learning system, implementing micro, meso, and macro learning loops, with advanced learning features. """ def __init__(self): """ Initializes AIVA's learning system. Includes initial axioms, confidence levels, and thresholds. """ self.axioms = { # Core beliefs about the world and how to respond "greeting": "Respond with a friendly greeting.", "farewell": "Respond with a polite farewell.", "question": "Attempt to answer the question accurately.", "default": "Acknowledge the input and offer assistance.", } self.confidence_levels = { # How confident AIVA is in its axioms "greeting": 0.8, "farewell": 0.9, "question": 0.6, "default": 0.7, } self.knowledge_graph = {} # Placeholder for a knowledge graph self.performance_metrics = [] # Stores performance data (accuracy, response_time, etc.) self.performance_thresholds = { # Thresholds for triggering model updates "accuracy": 0.75, # Minimum acceptable accuracy "response_time": 2.0, # Maximum acceptable response time (seconds) "feedback_score": 0.8, # Average user feedback score (1 is best) } self.learning_rate = 0.1 # Rate at which confidence levels are updated self.concept_drift_threshold = 0.95 # Threshold for triggering concept drift detection self.skill_performance = {} # Tracks performance of specific skills (e.g., answering math questions) self.threshold_recalibration_rate = 0.05 # Rate at which thresholds are adjusted self.learning_rate_adaptation_rate = 0.05 #Rate at which learning rate is adjusted self.performance_history = [] #Stores recent performance metrics for drift detection self.performance_history_size = 50 #Size of performance history for drift detection def micro_loop(self, query, prediction, outcome, response_time=None, feedback_score=None, skill_used=None): """ Updates confidence levels based on the outcome of a single query. Performs online learning from every interaction. Updates skill performance. Args: query (str): The user's query. prediction (str): AIVA's prediction/response. outcome (bool): True if the prediction was successful, False otherwise. response_time (float, optional): Response time in seconds. Defaults to None. feedback_score (float, optional): User feedback score (1 is best). Defaults to None. skill_used (str, optional): The specific skill used to handle the query. Defaults to None. """ print("\n--- Micro Loop ---") print(f"Query: {query}") print(f"Prediction: {prediction}") print(f"Outcome: {'Successful' if outcome else 'Failed'}") # Determine which axiom was used (simplified for example) axiom_used = "default" # Assume default initially for key in self.axioms: if key.lower() in query.lower(): # Simple keyword matching axiom_used = key break print(f"Axiom Used: {axiom_used}") # Update confidence level self.adjust_axiom_confidence(axiom_used, outcome) # Update Knowledge Graph (Placeholder - implement your KG logic here) self.update_knowledge_graph(query, prediction, outcome) # Track Skill Performance if skill_used: self.track_skill_performance(skill_used, outcome) # Store performance metrics performance_data = {"accuracy": int(outcome), "response_time": response_time or 1.0, "feedback_score": feedback_score or 0.75} self.performance_metrics.append(performance_data) self.performance_history.append(performance_data) #Keep the performance history size consistent if len(self.performance_history) > self.performance_history_size: self.performance_history.pop(0) print(f"Updated Confidence for {axiom_used}: {self.confidence_levels[axiom_used]}") def adjust_axiom_confidence(self, axiom_used, outcome): """Adjusts the confidence level of an axiom based on the outcome.""" if outcome: self.confidence_levels[axiom_used] = min(1.0, self.confidence_levels[axiom_used] + self.learning_rate) # Increase confidence else: self.confidence_levels[axiom_used] = max(0.0, self.confidence_levels[axiom_used] - self.learning_rate) # Decrease confidence def update_knowledge_graph(self, query, prediction, outcome): """Placeholder for knowledge graph update logic.""" # In a real system, this would involve extracting entities and relationships from the query # and prediction, and updating the knowledge graph accordingly. # For example: # if outcome: # extract entities and relationships from query and prediction # add/update nodes and edges in the knowledge graph pass # Replace with your KG update logic def track_skill_performance(self, skill_used, outcome): """Tracks the performance of specific skills.""" if skill_used not in self.skill_performance: self.skill_performance[skill_used] = {"correct": 0, "total": 0} self.skill_performance[skill_used]["total"] += 1 if outcome: self.skill_performance[skill_used]["correct"] += 1 accuracy = self.skill_performance[skill_used]["correct"] / self.skill_performance[skill_used]["total"] print(f"Skill '{skill_used}' Accuracy: {accuracy}") def meso_loop(self, session_data): """ Analyzes patterns within a session to generate insights and update axioms. Args: session_data (list): A list of (query, prediction, outcome) tuples from a session. """ print("\n--- Meso Loop ---") print(f"Analyzing session data: {session_data}") # Simple pattern analysis (example: frequent misinterpretation of a specific word) misinterpreted_words = {} for query, prediction, outcome in session_data: if not outcome: words = query.split() for word in words: if word in misinterpreted_words: misinterpreted_words[word] += 1 else: misinterpreted_words[word] = 1 # Identify the most frequently misinterpreted word if misinterpreted_words: most_misinterpreted_word = max(misinterpreted_words, key=misinterpreted_words.get) print(f"Potential Insight: Frequently misinterpreted word: {most_misinterpreted_word}") # Update Axioms (example: add a specific rule for that word) new_axiom_key = f"handle_{most_misinterpreted_word}" new_axiom_value = f"Clarify the meaning of '{most_misinterpreted_word}' in context." self.axioms[new_axiom_key] = new_axiom_value self.confidence_levels[new_axiom_key] = 0.5 # Initial confidence print(f"Updated Axioms: Added new axiom '{new_axiom_key}' with value '{new_axiom_value}'") else: print("No significant patterns found in the session data.") def macro_loop(self, learning_sources): """ Performs a comprehensive review of performance, updates the model, and adjusts thresholds. Args: learning_sources (dict): A dictionary containing learning sources like user feedback, validation outcomes, surprise events, and performance metrics. """ print("\n--- Macro Loop ---") print(f"Learning Sources: {learning_sources}") # 1. Analyze Performance Metrics if self.performance_metrics: average_accuracy = sum([m['accuracy'] for m in self.performance_metrics]) / len(self.performance_metrics) average_response_time = sum([m['response_time'] for m in self.performance_metrics]) / len(self.performance_metrics) average_feedback_score = sum([m['feedback_score'] for m in self.performance_metrics]) / len(self.performance_metrics) else: average_accuracy = 0 average_response_time = 0 average_feedback_score = 0 print(f"Average Accuracy: {average_accuracy}") print(f"Average Response Time: {average_response_time}") print(f"Average Feedback Score: {average_feedback_score}") # 2. Update Model (Simplified - can be replaced with actual model training) self.adapt_learning_rate(average_accuracy) # 3. Adjust Thresholds (Example: Based on Surprise Events) if learning_sources['surprise_events']: print("Handling surprise events...") for event in learning_sources['surprise_events']: if "slow response" in event.lower(): self.performance_thresholds['response_time'] = max(0.5, self.performance_thresholds['response_time'] * 0.9) # Reduce threshold print(f"Tightening response time threshold to {self.performance_thresholds['response_time']}") # 4. Incorporate User Feedback if learning_sources['user_feedback']: print("Incorporating user feedback...") for feedback in learning_sources['user_feedback']: if feedback['type'] == 'new_axiom': self.axioms[feedback['axiom_key']] = feedback['axiom_value'] self.confidence_levels[feedback['axiom_key']] = 0.5 # Initial confidence print(f"Added new axiom from feedback: {feedback['axiom_key']}") # 5. Automatic Threshold Recalibration self.recalibrate_thresholds(average_accuracy, average_response_time, average_feedback_score) # 6. Concept Drift Detection self.detect_concept_drift() print("Macro Loop Complete.") #Clear performance metrics for the next macro loop self.performance_metrics = [] def adapt_learning_rate(self, average_accuracy): """Adapts the learning rate based on the average accuracy.""" if average_accuracy < self.performance_thresholds['accuracy']: self.learning_rate = min(0.5, self.learning_rate * (1 + self.learning_rate_adaptation_rate)) # Increase learning rate, but cap it print(f"Accuracy below threshold. Increasing learning rate to {self.learning_rate}") else: self.learning_rate = max(0.01, self.learning_rate * (1 - self.learning_rate_adaptation_rate)) # Decrease learning rate if above threshold print(f"Accuracy above threshold. Decreasing learning rate to {self.learning_rate}") def recalibrate_thresholds(self, average_accuracy, average_response_time, average_feedback_score): """Recalibrates performance thresholds based on recent performance.""" # Adjust accuracy threshold self.performance_thresholds['accuracy'] = max(0.1, min(0.9, self.performance_thresholds['accuracy'] + (average_accuracy - self.performance_thresholds['accuracy']) * self.threshold_recalibration_rate)) print(f"Recalibrated Accuracy Threshold: {self.performance_thresholds['accuracy']}") # Adjust response time threshold self.performance_thresholds['response_time'] = max(0.5, self.performance_thresholds['response_time'] + (average_response_time - self.performance_thresholds['response_time']) * self.threshold_recalibration_rate) print(f"Recalibrated Response Time Threshold: {self.performance_thresholds['response_time']}") # Adjust feedback score threshold self.performance_thresholds['feedback_score'] = max(0.1, min(1.0, self.performance_thresholds['feedback_score'] + (average_feedback_score - self.performance_thresholds['feedback_score']) * self.threshold_recalibration_rate)) print(f"Recalibrated Feedback Score Threshold: {self.performance_thresholds['feedback_score']}") def detect_concept_drift(self): """Detects concept drift using a simple statistical test (e.g., comparing means).""" if len(self.performance_history) < self.performance_history_size: print("Not enough data to perform concept drift detection.") return # Split the performance history into two windows window1 = self.performance_history[:self.performance_history_size // 2] window2 = self.performance_history[self.performance_history_size // 2:] # Calculate the mean accuracy for each window mean_accuracy1 = np.mean([m['accuracy'] for m in window1]) mean_accuracy2 = np.mean([m['accuracy'] for m in window2]) # Perform a simple drift detection test (e.g., compare the difference in means) drift_score = abs(mean_accuracy1 - mean_accuracy2) print(f"Concept Drift Score: {drift_score}") if drift_score > (1 - self.concept_drift_threshold): print("Concept Drift Detected! Triggering model adaptation...") # In a real system, this would trigger more significant model retraining or adaptation. # For now, we'll just increase the learning rate. self.learning_rate = min(0.5, self.learning_rate * 1.5) print(f"Increased learning rate to {self.learning_rate} due to concept drift.") def generate_surprise_event(self): """Simulates a surprise event for testing.""" surprise_events = [ "Unexpected surge in user traffic caused slow response times.", "A new type of query emerged that the system was not trained on.", "System encountered an error due to an unusual input format.", "A change in the external API caused incorrect information retrieval." ] return random.choice(surprise_events) def get_state(self): """Returns the current state of the learning system.""" return { "axioms": self.axioms, "confidence_levels": self.confidence_levels, "knowledge_graph": self.knowledge_graph, "performance_thresholds": self.performance_thresholds, "learning_rate": self.learning_rate, "skill_performance": self.skill_performance } def set_state(self, state): """Sets the state of the learning system.""" self.axioms = state["axioms"] self.confidence_levels = state["confidence_levels"] self.knowledge_graph = state["knowledge_graph"] self.performance_thresholds = state["performance_thresholds"] self.learning_rate = state["learning_rate"] self.skill_performance = state["skill_performance"] # Example Usage if __name__ == "__main__": aiva = AIVAContinuousLearningSystem() # Micro Loop Example aiva.micro_loop("Hello", "Hi there!", True, response_time=0.5, feedback_score=0.9) aiva.micro_loop("What is 2 + 2?", "4", True, response_time=1.2, feedback_score=0.8, skill_used="math") aiva.micro_loop("What is the capital of France?", "I don't know.", False, response_time=2.8, feedback_score=0.6) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is the capital of France?", "Paris", True, response_time=1.5, feedback_score=0.7) aiva.micro_loop("What is 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