trainStep

Performs incremental learning from a single user interaction event.

This method implements the core online learning mechanism that allows the AI to continuously adapt to user behavior patterns without requiring batch training or external data processing. Each interaction contributes to the model's understanding of user preferences and behavioral patterns.

Training Pipeline:

1. Validation: Ensures the engine is properly initialized and ready for training

2. Data Preprocessing: Converts raw interaction data into numerical features

3. Forward Pass: Computes current model predictions for the input

4. Backpropagation: Updates model weights based on prediction error

5. History Management: Maintains context for future predictions

Asynchronous Design: Training operations are launched asynchronously to prevent blocking the main application thread. This ensures responsive user interfaces even during intensive learning operations. The returned Job allows callers to track training completion if needed.

Thread Safety: All model modifications are protected by mutex locks to prevent race conditions when multiple training operations might occur concurrently. This ensures model consistency and prevents corruption of the neural network state.

Learning Strategy: The current implementation uses:

• Immediate weight updates for real-time adaptation

• Circular buffer for maintaining interaction context

• Hash-based feature extraction for pattern recognition

• Incremental statistics tracking for progress monitoring

Future Enhancement Points: When migrating to the full KotlinDL implementation:

• Replace hash-based features with learned embeddings

• Implement mini-batch training for stability

• Add regularization techniques to prevent overfitting

• Include attention mechanisms for context awareness

Parameters

See also