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.
|Number of pages||5|
|Journal||IJCAI International Joint Conference on Artificial Intelligence|
|State||Published - 1 Dec 1997|
|Event||15th International Joint Conference on Artificial Intelligence, IJCAI 1997 - Nagoya, Aichi, Japan|
Duration: 23 Aug 1997 → 29 Aug 1997