AI Behavior Model

Overview

The AI system uses TensorFlow Lite for efficient inference on the Raspberry Pi 5 with AI HAT+ acceleration. Training is performed on Mac Mini M1, with the trained models deployed to the Raspberry Pi.

Core Components

1. Model Architecture
2. Input: Game state features
3. Output: Action probabilities
4. Format: TensorFlow Lite

5. Size: Optimized for edge deployment

6. Decision Making

   class BattleAI:
       def __init__(self, model_path: str):
           self.interpreter = tf.lite.Interpreter(model_path=model_path)
           self.interpreter.allocate_tensors()
   
       def decide_action(self, state: GameState, difficulty: float) -> Action:
           """Process game state and return next action"""
           features = self.extract_features(state)
           predictions = self.run_inference(features)
           return self.select_action(predictions, difficulty)
   

7. Difficulty Scaling

   def scale_difficulty(predictions: np.ndarray, difficulty: float) -> np.ndarray:
       """Adjust action selection based on difficulty level (0.2-0.95)"""
       # Simple temperature scaling
       return predictions ** (1.0 / difficulty)
   

Training Pipeline

1. Data Collection
2. Battle records
3. Action outcomes

4. Win/loss statistics

5. Training Environment

6. Platform: Mac Mini M1
7. Framework: TensorFlow

8. Output: TFLite model

9. Model Deployment

   def deploy_model(model_path: str):
       """Deploy model to Raspberry Pi"""
       interpreter = tf.lite.Interpreter(model_path=model_path)
       interpreter.allocate_tensors()
   
       # Verify model
       input_details = interpreter.get_input_details()
       output_details = interpreter.get_output_details()
       return interpreter
   

State Management

1. Feature Extraction

   def extract_features(state: GameState) -> np.ndarray:
       """Extract relevant features from game state"""
       features = [
           state.player.health / 100.0,
           state.opponent.health / 100.0,
           state.player.energy / 100.0,
           *encode_status_effects(state.player.status),
           *encode_status_effects(state.opponent.status)
       ]
       return np.array(features, dtype=np.float32)
   

2. Action Selection

   def select_action(predictions: np.ndarray, difficulty: float) -> Action:
       """Select action based on model predictions and difficulty"""
       scaled = scale_difficulty(predictions, difficulty)
       action_idx = np.random.choice(len(scaled), p=scaled)
       return ACTIONS[action_idx]
   

Integration

1. WebSocket Handler

   class AIHandler:
       def __init__(self, model_path: str):
           self.ai = BattleAI(model_path)
   
       async def handle_turn(self, state: dict, difficulty: float):
           """Handle turn request from game server"""
           action = self.ai.decide_action(state, difficulty)
           return {'action': action.to_dict()}
   

2. Performance Monitoring

   class AIMonitor:
       def __init__(self):
           self.inference_times = []
   
       def log_inference(self, duration: float):
           """Log inference time"""
           self.inference_times.append(duration)
   

Related Documentation

• Training Pipeline
• Difficulty System
• Hardware Configuration

Version History

• v1.0: Initial behavior model
• v1.1: Added personality system
• v1.2: Hardware optimization
• v1.3: Hailo-specific optimizations and hardware acceleration details
• v2.0: Simplified requirements and updated AI behavior model