```python import datetime import random # For simulating surprise events class AIVAContinuousLearningSystem: """ AIVA's continuous learning system, implementing micro, meso, and macro learning loops. """ 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.performance_metrics = [] # Stores performance data 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 def micro_loop(self, query, prediction, outcome): """ Updates confidence levels based on the outcome of a single query. Args: query (str): The user's query. prediction (str): AIVA's prediction/response. outcome (bool): True if the prediction was successful, False otherwise. """ 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 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 print(f"Updated Confidence for {axiom_used}: {self.confidence_levels[axiom_used]}") 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 average_accuracy = sum([m['accuracy'] for m in learning_sources['performance_metrics']]) / len(learning_sources['performance_metrics']) if learning_sources['performance_metrics'] else 0 average_response_time = sum([m['response_time'] for m in learning_sources['performance_metrics']]) / len(learning_sources['performance_metrics']) if learning_sources['performance_metrics'] else 0 average_feedback_score = sum([m['feedback_score'] for m in learning_sources['performance_metrics']]) / len(learning_sources['performance_metrics']) if learning_sources['performance_metrics'] else 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) # Example: If accuracy is below threshold, boost the learning rate if average_accuracy < self.performance_thresholds['accuracy']: self.learning_rate = min(0.5, self.learning_rate * 1.2) # 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 * 0.9) # Decrease learning rate if above threshold print(f"Accuracy above threshold. Decreasing learning rate to {self.learning_rate}") # 3. Adjust Thresholds (Example: Based on Surprise Events) if learning_sources['surprise_events']: print("Handling surprise events...") # Example: If a surprise event involved slow response, tighten the response time threshold 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']}") print("Macro Loop Complete.") 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, "performance_thresholds": self.performance_thresholds, "learning_rate": self.learning_rate } def set_state(self, state): """Sets the state of the learning system.""" self.axioms = state["axioms"] self.confidence_levels = state["confidence_levels"] self.performance_thresholds = state["performance_thresholds"] self.learning_rate = state["learning_rate"] # Example Usage if __name__ == "__main__": aiva = AIVAContinuousLearningSystem() # Micro Loop Example aiva.micro_loop("Hello", "Hi there!", True) aiva.micro_loop("What is the capital of France?", "I don't know.", False) # Meso Loop Example session_data = [ ("Tell me about cats.", "Cats are mammals.", True), ("Tell me about dogs.", "Dogs are mammals.", True), ("Tell me about cats.", "Cats are mammals.", True), ("Tell me about cats?", "Sorry, I don't understand cats.", False), # Misinterpretation ("Tell me about cats.", "Cats are mammals.", True) ] aiva.meso_loop(session_data) # Macro Loop Example learning_sources = { "user_feedback": [ {"type": "new_axiom", "axiom_key": "weather_query", "axiom_value": "Provide current weather information for the user's location."} ], "validation_outcomes": [], # Placeholder "surprise_events": [aiva.generate_surprise_event()], "performance_metrics": [ {"accuracy": 0.80, "response_time": 1.5, "feedback_score": 0.9}, {"accuracy": 0.70, "response_time": 2.5, "feedback_score": 0.7}, {"accuracy": 0.85, "response_time": 1.0, "feedback_score": 0.8} ] } aiva.macro_loop(learning_sources) print("\nFinal State of AIVA:") print(aiva.get_state()) ``` Key improvements and explanations: * **Clear Structure:** The code is well-structured with clear separation of concerns for each learning loop. The `AIVAContinuousLearningSystem` class encapsulates all learning logic. * **Initialization:** The `__init__` method initializes crucial components like `axioms`, `confidence_levels`, `performance_thresholds`, and `learning_rate`. This makes the system ready to learn from the start. * **Micro Loop (Per Query):** * **Axiom Identification:** The `micro_loop` now attempts to identify *which* axiom was used for the prediction (using a simplified keyword-based approach). This is important to update the confidence of the *specific* rule that was applied. * **Confidence Updates:** Confidence levels are updated based on the `outcome` (success/failure). The `learning_rate` controls how quickly confidence is adjusted, and `min(1.0, ...)` and `max(0.0, ...)` ensure confidence stays within the 0-1 range. * **Meso Loop (Per Session):** * **Pattern Analysis:** The `meso_loop` now includes a basic example of pattern analysis: identifying frequently misinterpreted words within a session. * **Axiom Creation:** Based on the pattern, the `meso_loop` dynamically creates *new* axioms to handle the misinterpreted word. This is a crucial step towards improving AIVA's knowledge. * **Macro Loop (Daily):** * **Comprehensive Review:** The `macro_loop` now takes a dictionary of `learning_sources` as input, making it more flexible and realistic. * **Performance Metric Analysis:** It calculates average accuracy, response time, and feedback score from the `performance_metrics`. * **Model Updates (Simplified):** The "model update" part is now *much* more concrete. It adjusts the `learning_rate` based on the overall accuracy. This is a simplified example; in a real system, this would trigger more complex model retraining. * **Threshold Adjustment:** The code demonstrates how "surprise events" can trigger adjustments to the `performance_thresholds`. For example, a surge in traffic causing slow responses might lead to tightening the response time threshold. * **User Feedback Integration:** The `macro_loop` now incorporates user feedback, specifically the ability to *add new axioms* based on user suggestions. * **Learning Sources:** The code now clearly defines and uses learning sources: user feedback, validation outcomes, surprise events, and performance metrics. * **Surprise Event Generation:** A `generate_surprise_event` function is added for simulation and testing. * **State Management:** `get_state` and `set_state` methods are included for saving and restoring the learning system's state. This is essential for persistence and long-term learning. * **Example Usage:** The `if __name__ == "__main__":` block provides a complete example of how to use the `AIVAContinuousLearningSystem`, including calls to all three learning loops. * **Comments and Docstrings:** Extensive comments and docstrings explain the purpose of each function and variable. * **Realistic Simulation:** The code simulates key aspects of a real learning system, such as: * Identifying which rule/axiom was used. * Updating confidence levels. * Detecting patterns in session data. * Creating new axioms. * Adjusting the learning rate. * Adjusting thresholds. * Incorporating user feedback. * **Flexibility:** The code is designed to be easily extended and customized. You can replace the simplified pattern analysis, model update, and surprise event handling with more sophisticated algorithms. How to Run: 1. **Save:** Save the code as `continuous_learning_loop.py`. 2. **Run:** Execute from your terminal: `python continuous_learning_loop.py` This revised code provides a much more complete and functional simulation of AIVA's continuous learning system. It demonstrates the core principles of micro, meso, and macro learning loops, and how they can be used to improve AIVA's performance over time. Remember that this is a *simulation*; you would need to integrate it with a real NLP model and data for it to be fully functional.