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Applying integer programming to AI planning

Published online by Cambridge University Press:  06 March 2012

THOMAS VOSSEN ,
MICHAEL BALL ,
AMNON LOTEM  and
DANA NAU
THOMAS VOSSEN
Affiliation:
Robert H. Smith School of Business and Institute for Systems Research, Email: mball@rhsmith.umd.edu, tvossen@rhsmith.umd.edu
MICHAEL BALL
Affiliation:
Robert H. Smith School of Business and Institute for Systems Research, Email: mball@rhsmith.umd.edu, tvossen@rhsmith.umd.edu
AMNON LOTEM
Affiliation:
Department of Computer Science and Institute for Systems Research, University of Maryland, College Park, MD 20742, USA, Email: nau@cs.umd.edu, lotem@cs.umd.edu
DANA NAU
Affiliation:
Department of Computer Science and Institute for Systems Research, University of Maryland, College Park, MD 20742, USA, Email: nau@cs.umd.edu, lotem@cs.umd.edu
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Abstract

Despite the historical difference in focus between AI planning techniques and Integer Programming (IP) techniques, recent research has shown that IP techniques show significant promise in their ability to solve AI planning problems. This paper provides approaches to encode AI planning problems as IP problems, describes some of the more significant issues that arise in using IP for AI planning, and discusses promising directions for future research.

Type
Review Article
Information
The Knowledge Engineering Review , Volume 15 , Issue 1 , March 2000 , pp. 85 - 100
Copyright
© 2000 Cambridge University Press

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Footnotes

This work was supported in part, by the following grants and contracts: Army Research Laboratory DAAL0197-K0135, Naval Research Laboratory N00173981G007, Air Force Research Laboratory F306029910013, and NSF DMI-9713718.