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Integer optimization models of AI planning problems

Published online by Cambridge University Press:  06 March 2012

HENRY KAUTZ
Affiliation:
AT&T Shannon Labs, 180 Park Avenue, Florham Park, NJ 07932, USA (email: [email protected])
JOACHIM P. WALSER
Affiliation:
i2 Technologies, 11701 Luna Road, Dallas, TX 75234, USA (email: [email protected])

Abstract

This paper describes ILP-PLAN, a framework for solving AI planning problems represented as integer linear programs. ILP-PLAN extends the planning as satisfiability framework to handle plans with resources, action costs, and complex objective functions. We show that challenging planning problems can be effectively solved using both traditional branch-and-bound integer programming solvers and efficient new integer local search algorithms. ILP-PLAN can find better quality solutions for a set of hard benchmark logistics planning problems than had been found by any earlier system.

Type
Review Article
Copyright
© 2000 Cambridge University Press

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