Handling Working Memory Knowledge Through a Consultant-Level Resource Management Strategy

Working memory is an important component of cognition that influences key cognitive processes, such as language. As such, working memory should play a key role in cognitive models for language-capable robots. The ways in which working memory buffers are organized within a robot's architecture can inform processes such as Referring Expression Generation. Thus, it is important to understand how information and resources within working memory may be organized to lead to human-like robotic language. Previous work on the DIARC cognitive architecture described an entity-level, feature-based working memory framework in which each known entity had its own dedicated working memory buffer. This paper expands on that framework and proposes a new resource management strategy in which sets of entities that belong to the same type share a single working memory buffer. We end the paper with a brief discussion of how this novel strategy compares to the previously implemented entity-level strategy.