Readers of this newsgroup may be interested in the following paper recently
published by JAIR:
Hauskrecht, M. (2000)
"Value-Function Approximations for Partially Observable Markov Decision
Processes", Volume 13, pages 33-94.
Available in PDF, PostScript and compressed PostScript.
For quick access via your WWW browser, use this URL:
http://www.jair.org/abstracts/hauskrecht00a.html
More detailed instructions are below.
Abstract: Partially observable Markov decision processes (POMDPs)
provide an elegant mathematical framework for modeling complex
decision and planning problems in stochastic domains in which states
of the system are observable only indirectly, via a set of imperfect
or noisy observations. The modeling advantage of POMDPs, however,
comes at a price -- exact methods for solving them are computationally
very expensive and thus applicable in practice only to very simple
problems. We focus on efficient approximation (heuristic) methods that
attempt to alleviate the computational problem and trade off accuracy
for speed. We have two objectives here. First, we survey various
approximation methods, analyze their properties and relations and
provide some new insights into their differences. Second, we present a
number of new approximation methods and novel refinements of existing
techniques. The theoretical results are supported by experiments on a
problem from the agent navigation domain.
The article is available via:
-- comp.ai.jair.papers (also see comp.ai.jair.announce)
-- World Wide Web: The URL for our World Wide Web server is
http://www.jair.org/
For direct access to this article and related files try:
http://www.jair.org/abstracts/hauskrecht00a.html
-- Anonymous FTP from either of the two sites below.
Carnegie-Mellon University (USA):
ftp://ftp.cs.cmu.edu/project/jair/volume13/hauskrecht00a.ps
The University of Genoa (Italy):
ftp://ftp.mrg.dist.unige.it/pub/jair/pub/volume13/hauskrecht00a.ps
The compressed PostScript file is named hauskrecht00a.ps.Z (532K)
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