Functional Interactions Between Memory and Recognition Judgments

One issue facing agents that accumulate large bodies of knowledge is determining whether they have knowledge that is relevant to its current goals. Performing comprehensive searches of long-term memory in every situation can be computationally expensive and disruptive to task reasoning. In this paper, we demonstrate that the recognition judgment — a heuristic for whether memory structures have been previously perceived — can serve as a low-cost indicator of the existence of potentially relevant knowledge. We present an approach for computing both context-dependent and contextindependent recognition judgments using processes and data shared with declarative memories. We then describe an initial, efficient implementation in the Soar cognitive architecture and evaluate our system in a word sense disambiguation task, showing that it reduces the number of memory searches without degrading agent performance.

Authors

• Justin Li
• Nate Derbinsky
• John Laird

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