Performance evaluation of declarative memory systems in Soar

Abstract

performance evaluation, memory and learning, cognitive architecture ABSTRACT: A rarely studied issue with using persistent computational models is whether the underlying computational mechanisms scale as knowledge is accumulated through learning. In this paper we evaluate the declarative memories of Soar: working memory, semantic memory, and episodic memory, using a detailed simulation of a mobile robot running for one hour of real-time. Our results indicate that our implementation is sufficient for tasks of this length. Moreover our system executes orders of magnitudes faster than real-time, with relatively modest storage requirements. We also project the computational resources required for extended operations. 1.