Craig Boutilier, Ronen I. Brafman and Christopher Geib
Department of Computer Science
University of British Columbia
Vancouver, BC, CANADA, V6T 1Z4
email: cebly,brafman,geib@cs.ubc.ca
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.
To appear, IJCAI-97, Nagoya, Japan
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