Readers of this mailing list may be interested in the following
article which was recently published by JAIR:
Boutilier, C., Dean, T. and Hanks, S. (1999)
"Decision-Theoretic Planning: Structural Assumptions and Computational Leverage",
Volume 11, pages 1-94.
Available in PDF, PostScript and compressed PostScript.
For quick access via your WWW browser, use this URL:
http://www.jair.org/abstracts/boutilier99a.html
More detailed instructions are below.
Abstract: Planning under uncertainty is a central problem in the study of
automated sequential decision making, and has been addressed by
researchers in many different fields, including AI planning, decision
analysis, operations research, control theory and economics. While
the assumptions and perspectives adopted in these areas often differ
in substantial ways, many planning problems of interest to researchers
in these fields can be modeled as Markov decision processes (MDPs)
and analyzed using the techniques of decision theory.
This paper presents an overview and synthesis of MDP-related methods,
showing how they provide a unifying framework for modeling many
classes of planning problems studied in AI. It also describes
structural properties of MDPs that, when exhibited by particular
classes of problems, can be exploited in the construction of optimal
or approximately optimal policies or plans. Planning problems
commonly possess structure in the reward and value functions used to
describe performance criteria, in the functions used to describe state
transitions and observations, and in the relationships among features
used to describe states, actions, rewards, and observations.
Specialized representations, and algorithms employing these
representations, can achieve computational leverage by exploiting
these various forms of structure. Certain AI techniques -- in
particular those based on the use of structured, intensional
representations -- can be viewed in this way. This paper surveys
several types of representations for both classical and
decision-theoretic planning problems, and planning algorithms that
exploit these representations in a number of different ways to ease
the computational burden of constructing policies or plans. It focuses
primarily on abstraction, aggregation and decomposition techniques
based on AI-style representations.
The article is available via:
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http://www.jair.org/
For direct access to this article and related files try:
http://www.jair.org/abstracts/boutilier99a.html
-- Anonymous FTP from either of the two sites below.
Carnegie-Mellon University (USA):
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The University of Genoa (Italy):
ftp://ftp.mrg.dist.unige.it/pub/jair/pub/volume11/boutilier99a.ps
The compressed PostScript file is named boutilier99a.ps.Z (527K)
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