Nate Derbinsky

Efficiently Implementing Episodic Memory

Endowing an intelligent agent with an episodic memory affords it a multitude of cognitive capabilities. However, providing efficient storage and retrieval in a task-independent episodic memory presents considerable theoretical and practical challenges. We characterize the computational issues bounding an episodic memory. We explore whether even with intractable asymptotic growth, it is possible to develop efficient algorithms and data structures for episodic memory systems that are practical for real-world tasks. We present and evaluate formal and empirical results using Soar-EpMem: a task-independent integration of episodic memory with Soar 9, providing a baseline for graph-based, task-independent episodic memory systems.

Effective and efficient forgetting of learned knowledge in Soar's working and procedural memories

Effective management of learned knowledge is a challenge when modeling human-level behavior within complex, temporally extended tasks. This work evaluates one approach to this problem: forgetting knowledge that is not in active use (as determined by base-level activation) and can likely be reconstructed if it becomes relevant. We apply this model to the working and procedural memories of Soar. When evaluated in simulated, robotic exploration and a competitive, multi-player game, these policies improve model reactivity and scaling while maintaining reasoning competence. To support these policies for real-time modeling, we also present and evaluate a novel algorithm to efficiently forget items from large memory stores while preserving base-level fidelity.