Craig Boutilier
Department of Computer Science
University of British Columbia
Vancouver, BC, CANADA, V6T 1Z4
email: cebly@cs.ubc.ca
Thomas Dean
Department of Computer Science
Brown University
Providence, RI 02912, U.S.A.
email: tld@cs.brown.edu
Steve Hanks
Department of Computer Science and Engineering
University of Washington
Seattle, WA 98195 U.S.A.
email: hanks@cs.washington.edu
Abstract
The problem of planning under uncertainty has been addressed
by researchers in many different fields, adopting rather
different perspectives on the problem. Unfortunately, these
researchers are not always aware of the relationships among
these different problem formulations,
often resulting in confusion and duplicated effort.
Many probabilistic planning or decision making problems can be characterized
as a class of Markov decision processes that allow for
significant compression in representing the underlying system dynamics. It
is for this class of problems that we as experts in intensional
representations are advantageously positioned to contribute
efficient solution methods. This paper provides a general
characterization of the representational requirements for this class
of problems, and we describe how to achieve computational leverage
using representations that make different types
of dependency information explicit.
Unpublished Manuscript
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