A case study of knowledge integration across multiple memories in Soar

Online perception, behavior, and learning in complex domains require an intelligent agent to quickly and reliably access different types of knowledge. A cognitive architecture, therefore, must implement a diverse set of memories that are optimized for storing, accessing, and learning these different types of knowledge. In this paper, we describe a complex Soar agent that uses and learns multiple types of knowledge while interacting with a human in a real-world domain. Our hypothesis is that a diverse set of memories is required for the different types of knowledge. We first present the agent's processing, highlighting the types of knowledge used for each phase. We then present Soar's memories and identify which memory is used for each type of knowledge. We also analyze which properties of each memory make it appropriate for the knowledge it encodes. These include procedural, semantic, and episodic knowledge, all of which play critical roles in the agent's ability to learn and extend its capabilities. We conclude with a summary of our analysis, and conclude that a diversity of memory systems and knowledge are useful for supporting general, integrated intelligence.