Implement Self-Search Capabilities for Agent Memory

[csmangum]

Currently, our agent's memory system is passively queried by external functions, but the agent itself lacks the ability to introspectively search and manipulate its own memory space. This issue proposes implementing a "self-search" capability that would allow the agent to actively explore its own embeddings, retrieve relevant memories, and generate counterfactuals based on internal queries.

Core Concept

Develop a metacognitive interface layer that enables the agent to:

1. Formulate queries about its own memory and experiences
2. Search its embedding space for relevant information
3. Retrieve and manipulate memories based on task contexts
4. Generate targeted counterfactuals based on self-directed inquiries

Implementation Components

1. Self-Query Interface

Description: An internal API that allows the agent to search its own memory embeddings using vector queries.

Implementation Ideas:

• Create a parameterized query construction mechanism that converts agent goals/questions into embedding space vectors
• Implement relevance scoring based on semantic similarity
• Develop context-sensitive query formulation (e.g., "recall similar situations where I had low resources")
• Enable compound queries that combine multiple constraints

Research Questions:

• How should queries be formulated to be most effective in embedding space?
• How to balance precision and recall in self-directed memory retrieval?
• Should query capability be a learned behavior or a built-in mechanism?

2. Metacognitive Attention

Description: Mechanisms for the agent to focus on specific regions or dimensions of its embedding space.

Implementation Ideas:

• Develop attention masks that highlight relevant dimensions in the embedding space
• Implement "memory spotlights" that dynamically focus on regions of interest
• Create mechanisms for zooming in/out of memory hierarchy (from specific episodes to general concepts)
• Build visualizations that show where attention is focused in the embedding space

Research Questions:

• What's the optimal attention mechanism for embedding space navigation?
• Can the agent learn to attend to the most relevant dimensions for different tasks?
• How does attention allocation affect learning and decision-making?

3. Memory-Guided Counterfactual Generation

Description: Allow the agent to generate targeted counterfactuals based on specific questions it poses to itself.

Implementation Ideas:

• Implement "what-if" operators that transform retrieved memories
• Create counterfactual generation guided by specific agent queries
• Build mechanisms to evaluate the utility of self-generated counterfactuals
• Develop an internal "dialogue" system for refining counterfactual questions

Research Questions:

• How does self-directed counterfactual generation compare to automatic/random generation?
• Can the agent learn to generate more useful counterfactuals over time?
• How to balance exploration vs. exploitation in counterfactual generation?

4. Memory Optimization Interface

Description: Tools for the agent to reorganize, consolidate, or prioritize memories based on utility.

Implementation Ideas:

• Create mechanisms for the agent to flag high-value memories for preservation
• Implement "memory consolidation" procedures that the agent can trigger
• Develop utility metrics that the agent can use to evaluate its own memory organization
• Build garbage collection routines that the agent can invoke for outdated memories

Research Questions:

• Can the agent effectively judge which memories are worth preserving?
• How does self-directed memory management compare to automatic approaches?
• What are the computational tradeoffs of giving the agent control over memory?

Technical Challenges

1. Recursive Complexity: Managing the computational complexity of self-reference without infinite loops
2. Efficiency Concerns: Ensuring that self-search operations remain computationally tractable
3. Evaluation Difficulty: Developing metrics to assess the quality of self-directed memory operations
4. Interface Design: Creating an intuitive internal API that the agent can effectively utilize

Potential Applications

1. Deliberate Learning: Agent explicitly searches for knowledge gaps and seeks to fill them
2. Self-Explanation: Ability to trace and explain reasoning by examining memory access patterns
3. Memory Debugging: Agent can identify and flag problematic or corrupted memories
4. Metacognitive Development: Foundation for higher-order reasoning about the agent's own knowledge
5. Dynamic Resource Allocation: More efficient use of memory resources based on task demands

Implementation Phases

Phase 1: Basic Self-Query

• Implement simple vector-based self-query mechanism
• Create basic visualization of query results
• Develop evaluation metrics for query relevance

Phase 2: Advanced Query Mechanisms

• Implement compound queries and filtering
• Add context-sensitive query formulation
• Develop query templates for common scenarios

Phase 3: Self-Directed Counterfactuals

• Implement counterfactual operators guided by queries
• Create evaluation framework for counterfactual quality
• Build feedback mechanisms for improving counterfactual generation

Phase 4: Memory Management Tools

• Implement memory consolidation and prioritization
• Develop memory organization strategies
• Create metrics for memory efficiency

Success Criteria

1. The agent can formulate meaningful queries about its own experiences
2. Self-directed memory retrieval shows better task relevance than random sampling
3. Agent-generated counterfactuals demonstrate higher utility for current tasks
4. The system shows improved memory efficiency with self-directed management
5. The agent can explain its decisions by referencing specific memories

Resources & References

• Singh, S., et al. (2020). "Introspective Models for Reinforcement Learning"
• Cox, M.T. (2005). "Metacognition in Computation: A Selected Research Review"
• Nelson, T.O. & Narens, L. (1990). "Metamemory: A Theoretical Framework and New Findings"
• Ba, J., et al. (2016). "Using Fast Weights to Attend to the Recent Past"
• Graves, A., et al. (2016). "Hybrid computing using a neural network with dynamic external memory"

Notes

This capability represents a significant step toward true metacognition in AI systems. By allowing the agent to introspect and manipulate its own memory representations, we're creating a foundation for more sophisticated self-awareness and cognitive control.

The self-search capability should be designed with appropriate constraints to prevent computational explosion while still providing flexibility. The initial implementation should focus on simple, well-defined query types before expanding to more complex metacognitive operations.