An Algorithm for Solving Constraint-Satisfaction Problems
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BibTeX
@MISC{Pang_analgorithm,
author = {Wanlin Pang and Scott D. Goodwin},
title = {An Algorithm for Solving Constraint-Satisfaction Problems},
year = {}
}
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Abstract
We introduce a new method, called constraint-directed-generate-and-test (CDGT ), for solving constraint satisfaction problems (CSPs). Instead of appending a new instantiation of one variable to a partial solution, as in traditional search methods, CDGT joins two sets of partial solutions and then checks against other relevant constraints to obtain a set of higher order partial solutions. CDGT is an efficient algorithm for finding all solutions of a CSP and it can be implemented for parallel execution. 1 Introduction Constraint satisfaction problems (CSPs) involve finding values for variables subject to constraints which permit combinations of values. Since many problems in AI and other areas of computer science can be formulated as CSPs, it has been a research subject for a long time and researchers have approached the subject in three directions: searching for the CSP's solutions from the possible solution space ([6, 4, 2]), reducing a CSP to a simpler and equivalent CSP ([8, 9, 11, ...
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