[UAI] JAIR: Nonapproximability for POMDPs

From: Judy Goldsmith (goldsmit@cs.uky.edu)
Date: Thu May 10 2001 - 10:43:22 PDT

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    Readers of this list may be interested in the following article that was
    just published in JAIR:

    Lusena, C., Goldsmith, J. and Mundhenk, M. (2001)
      "Nonapproximability Results for Partially Observable Markov Decision
    Processes",
       Volume 14, pages 83-103.

       Available in PDF, PostScript and compressed PostScript.
       For quick access via your WWW browser, use this URL:
         http://www.jair.org/abstracts/lusena01a.html
       More detailed instructions are below.

       Abstract: We show that for several variations of partially observable
       Markov decision processes, polynomial-time algorithms for finding
       control policies are unlikely to or simply don't have guarantees of
       finding policies within a constant factor or a constant summand of
       optimal. Here ``unlikely'' means ``unless some complexity classes
       collapse,'' where the collapses considered are P=NP, P=PSPACE, or
       P=EXP. Until or unless these collapses are shown to hold, any
       control-policy designer must choose between such performance
       guarantees and efficient computation.

    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/lusena01a.html

     -- Anonymous FTP from either of the two sites below.

        Carnegie-Mellon University (USA):
            ftp://ftp.cs.cmu.edu/project/jair/volume14/lusena01a.ps
        The University of Genoa (Italy):
            ftp://ftp.mrg.dist.unige.it/pub/jair/pub/volume14/lusena01a.ps

        The compressed PostScript file is named lusena01a.ps.Z (247K)

    For more information about JAIR, visit our WWW or FTP sites, or
    contact jair-ed@isi.edu



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