Prioritized goal decomposition of Markov decision processes: Toward a synthesis of classical and decision theoretic planning

Craig Boutilier, Ronen I. Brafman, Christopher Geib

Research output: Contribution to journalConference articlepeer-review

29 Scopus citations

Abstract

We describe an approach to goal decomposition for a certain class of Markov decision processes (MDPs). An abstraction mechanism is used to generate abstract MDPs 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 merging 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.

Original languageEnglish
Pages (from-to)1158-1162
Number of pages5
JournalIJCAI International Joint Conference on Artificial Intelligence
Volume2
StatePublished - 1 Dec 1997
Externally publishedYes
Event15th International Joint Conference on Artificial Intelligence, IJCAI 1997 - Nagoya, Aichi, Japan
Duration: 23 Aug 199729 Aug 1997

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