Effective and Efficient Management of Soar's Working Memory via Base-Level Activation

This paper documents a functionality-driven exploration of automatic working-memory management in Soar. We first derive and discuss desiderata that arise from the need to embed a mechanism for managing working memory within a general cognitive architecture that is used to develop real-time agents. We provide details of our mechanism, including the decay model and architecture-independent data structures and algorithms that are computationally efficient. Finally, we present empirical results, which demonstrate both that our mechanism performs with little computational overhead and that it helps maintain the reactivity of a Soar agent contending with long-term, autonomous simulated robotic exploration as it reasons using large amounts of acquired information.