Conservative and reward-driven behavior selection in a commonsense reasoning framework

Publication Type:
Conference Proceeding
Citation:
AAAI Fall Symposium - Technical Report, 2009, FS-09-05 pp. 14 - 19
Issue Date:
2009-12-01
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Comirit is a framework for commonsense reasoning that combines simulation, logical deduction and passive machine learning. While a passive, observation-driven approach to learning is safe and highly conservative, it is limited to interaction only with those objects that it has previously observed. In this paper we describe a preliminary exploration of methods for extending Comirit to allow safe action selection in uncertain situations, and to allow reward-maximizing selection of behaviors. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.
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