Adaptive/Evolutionary Search Strategy

[csmangum]

Implement an AdaptiveSearchStrategy that dynamically combines multiple search strategies and evolves their weights based on real-world agent usage and performance. This will allow the system to automatically favor the most effective strategies, adapting over time as agents interact with the memory system.

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Motivation

Currently, the CombinedSearchStrategy statically combines results from multiple strategies using fixed weights. However, different agents and use cases may benefit from different strategies, and the optimal combination may change over time. By introducing an adaptive, evolutionary approach, we can:

• Automatically discover the most effective search strategies for different agents and scenarios.
• Allow the system to improve itself based on feedback and usage statistics.
• Enable new strategies to compete and potentially become dominant if they prove effective.

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Requirements

1. New Strategy Class

• Create AdaptiveSearchStrategy in memory/search/strategies/adaptive.py.
• Inherit from CombinedSearchStrategy to reuse combination logic.

2. Usage and Performance Tracking

• Track which strategies are used for each search and their outcomes (e.g., relevance, user feedback, success rate).
• Maintain statistics per agent and globally.

3. Adaptive Weight Adjustment

• Implement an algorithm to periodically update strategy weights based on usage and/or performance.
  • Options: simple reinforcement (reward/punish), multi-armed bandit, or genetic/evolutionary algorithm.
• Allow configuration of update frequency and algorithm parameters.

4. Persistence

• Optionally, persist weights and statistics to disk so adaptation is not lost between runs.

5. Interface

• Expose methods to:
  • Manually adjust weights.
  • Retrieve current statistics and weights.
  • Reset adaptation state.

6. Integration

• Ensure the new strategy can be used interchangeably with existing strategies.
• Add documentation and usage examples.

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Implementation Plan

1. Scaffold AdaptiveSearchStrategy in a new file.
2. Add tracking for strategy usage and feedback.
3. Implement adaptive update logic (start with a simple reward-based system; make it pluggable for future algorithms).
4. Integrate persistence (e.g., using JSON or pickle).
5. Write tests to verify adaptation and weight updates.
6. Update documentation and provide usage examples.

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Example Pseudocode

class AdaptiveSearchStrategy(CombinedSearchStrategy):
    def __init__(self, strategies, weights=None, ...):
        super().__init__(strategies, weights)
        self.usage_stats = {...}
        self.performance_stats = {...}
        # Load persisted state if available

    def search(...):
        # Track which strategy is used and its result
        # Call super().search(...)
        # Update stats
        # Optionally update weights after each search
        pass

    def update_weights(self):
        # Adjust weights based on stats
        pass

    def save_state(self):
        # Persist weights and stats
        pass

    def load_state(self):
        # Load weights and stats
        pass

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Acceptance Criteria

• AdaptiveSearchStrategy exists and can be used as a drop-in replacement for CombinedSearchStrategy.
• Strategy weights adapt over time based on usage/performance.
• Statistics and weights can be persisted and restored.
• Documentation and usage examples are provided.
• Tests cover adaptation logic and persistence.

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References

• Current CombinedSearchStrategy implementation
• Multi-armed bandit algorithms
• Reinforcement learning basics