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Prioritized Goal Decomposition of Markov Decision Processes: Toward a Synthesis of Classical and Decision Theoretic Planning

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Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence. Planning and Scheduling. Pages 1156-1161.

Abstract

We describe an approach to goal decomposition for a certain class of Markov decision processes (MOPs) . An abstraction mechanism is used to generate abstract MOPs associated with different objectives, and several methods for merging the policies for these different objectives are considered. In one technique, causal (least-commitment) structures are generated for abstract policies and plan merg ing techniques, exploiting the relaxation of policy commitments reflected in this structure, are used to piece the results into a single policy. Abstract value functions provide guidance if plan repair is needed. This work makes some first steps toward the synthesis of classical and decision theoretic planning methods .

Craig Boutilier, Ronen I. Brafman, and Christopher W. Geib. 1997. Prioritized Goal Decomposition of Markov Decision Processes: Toward a Synthesis of Classical and Decision Theoretic Planning. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI-97. IJCAI, 1156–1161. https://ijcai.org/proceedings/1997/167
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