GSAT and Dynamic Backtracking

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GSAT and Dynamic Backtracking (1994)

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by Matthew L. Ginsberg , David A. McAllester

Venue:	Journal of Artificial Intelligence Research
Citations:	385 - 15 self

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@ARTICLE{Ginsberg94gsatand,
    author = {Matthew L. Ginsberg and David A. McAllester},
    title = {GSAT and Dynamic Backtracking},
    journal = {Journal of Artificial Intelligence Research},
    year = {1994},
    volume = {1},
    pages = {25--46}
}

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Abstract

There has been substantial recent interest in two new families of search techniques. One family consists of nonsystematic methods such as gsat; the other contains systematic approaches that use a polynomial amount of justification information to prune the search space. This paper introduces a new technique that combines these two approaches. The algorithm allows substantial freedom of movement in the search space but enough information is retained to ensure the systematicity of the resulting analysis. Bounds are given for the size of the justification database and conditions are presented that guarantee that this database will be polynomial in the size of the problem in question. 1 INTRODUCTION The past few years have seen rapid progress in the development of algorithms for solving constraintsatisfaction problems, or csps. Csps arise naturally in subfields of AI from planning to vision, and examples include propositional theorem proving, map coloring and scheduling problems. The probl...

Keyphrases

dynamic backtracking search space new technique contains systematic approach map coloring propositional theorem proving justification information constraintsatisfaction problem polynomial amount rapid progress past year justification database nonsystematic method enough information csps arise resulting analysis scheduling problem new family substantial recent interest search technique substantial freedom

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