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- W2784763367 abstract "One of the challenges for the constraint satisfaction community has been to develop an automated approach to solving Constraint Satisfaction Problems (CSPs) rather than creating specific algorithms for specific problems. Much of this work has concentrated on the development and improvement of general purpose backtracking techniques. However, the success of relatively simple local search techniques on larger satisfiability problems [Selman et al. 1992] and CSPs such as the n-queens [Minton et al. 1992] has caused interest in applying local search to constraint satisfaction. In this thesis we look at the usefulness of constraint weighting as a local search technique for constraint satisfaction. The work is based on the clause weighting ideas of Selman and Kautz [1993] and Morris [1993] and applies, evaluates and extends these ideas from the satisfiability domain to the more general domain of CSPs. Specifically, the contributions of the thesis are: • The introduction of a local search taxonomy. We examine the various better known local search techniques and recognise four basic strategies: restart, randomness, memory and weighting. • The extension of the CSP modelling framework. In order to represent and efficiently solve more realistic problems we extend the CSP modelling framework to include array-based domains and array-based domain use constraints. • The empirical evaluation of constraint weighting. We compare the performance of three constraint weighting strategies on a range of CSP and satisfiability problems and with several other local search techniques. We find that no one technique dominates in all problem domains." @default.
- W2784763367 created "2018-02-02" @default.
- W2784763367 creator A5045224660 @default.
- W2784763367 date "2000-01-01" @default.
- W2784763367 modified "2023-09-24" @default.
- W2784763367 title "Constraint Weighting Local Search for Constraint Satisfaction" @default.
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