Achieving far transfer in an integrated cognitive architecture

Authors:
Dan Shapiro;Tolga Könik;Paul O'Rorke
Affiliations:
Computational Learning Laboratory, CSLI, Stanford University, Stanford, CA;Computational Learning Laboratory, CSLI, Stanford University, Stanford, CA;Computational Learning Laboratory, CSLI, Stanford University, Stanford, CA
Venue:
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Year:
2008

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Abstract

Transfer is the ability to employ knowledge acquired in one task to improve performance in another. We study transfer in the context of the ICARUS cognitive architecture, which supplies diverse capabilities for execution, inference, planning, and learning. We report on an extension to ICARUS called representation mapping that transfers structured skills and concepts between disparate tasks that may not even be expressed with the same symbol set. We show that representation mapping is naturally integrated into ICARUS' cognitive processing loop, resulting in a system that addresses a qualitatively new class of problems by considering the relevance of past experience to current goals.