Extending soar with dissociated symbolic memories
Nate Derbinsky, John E. Laird · 2010
Abstract. 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. 1 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. 1