A Local Search Approach to Modelling and Solving Interval Algebra Problems
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Local search techniques have attracted considerable interest in the artificial intelligence community since the development of GSAT and the min-conflicts heuristic for solving propositional satisfiability (SAT) problems and binary constraint satisfaction problems (CSPs) respectively. Newer techniques, such as the discrete Langrangian method (DLM), have significantly improved on GSAT and can also be applied to general constraint satisfaction and optimization. However, local search has yet to be successfully employed in solving temporal constraint satisfaction problems (TCSPs). This paper argues that current formalisms for representing TCSPs are inappropriate for a local search approach, and proposes an alternative CSP-based end-point ordering model for temporal reasoning. The paper looks at modelling and solving problems formulated using Allen's interval algebra (IA) and proposes a new constraint weighting algorithm derived from DLM. Using a set of randomly generated IA problems, it is shown that local search outperforms existing consistency-enforcing algorithms on those problems that the existing techniques find most difficult.
Journal of Logic and Computation
© 2004 Oxford University Press. Please refer to the link for the definitive publisher-authenticated version.This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Journal of Logic and Computation following peer review. The definitive publisher-authenticated version J Logic Computation 2004 14: 93-112 is available online at: http://logcom.oxfordjournals.org/cgi/reprint/14/1/93