State-space Planning by Integer Optimization (1999)
| Venue: | In Proceedings of the Sixteenth National Conference on Artificial Intelligence |
| Citations: | 58 - 0 self |
BibTeX
@INPROCEEDINGS{Kautz99state-spaceplanning,
author = {Henry Kautz and Joachim P. Walser},
title = {State-space Planning by Integer Optimization},
booktitle = {In Proceedings of the Sixteenth National Conference on Artificial Intelligence},
year = {1999},
pages = {526--533},
publisher = {AAAI Press}
}
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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 branchand -bound IP 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. 1 Introduction In recent years the AI community witnessed the unexpected success of satisfiability testing as a method for solving state-space planning problems (Weld 1999). Kautz and Selman (1996) demonstrated that in certain computationally challenging domains, the approach of axiomatizing problems in propositional logic and solving them with general randomized SAT algorithms (SATPLAN) was competitive with or superior to the ...
