Extending Soar with Dissociated Symbolic Memories

Over long lifetimes, learning agents accumulate large stores of knowledge. To support human-level decision-making, their cognitive architectures must efficiently manage this experience and bring to bear pertinent data to act in the world.Prior psychological and computational work suggests the need for multiple, dissociated memory systems, citing significant functional and computational tradeoffs that arise when implementing a single memory mechanism for different types of learning tasks. In this context, we develop a memory-centric analysis of Soar 9, a general cognitive architecture that incorporates multiple long-term memories. In this analysis, we explore the functional abilities, computational opportunities, and theoretical challenges entailed by integrating a diverse set of symbolic memory systems.