Framework for guided complete search for solving constraint satisfaction problems and some of its instances
Refereed conference paper presented and published in conference proceedings


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AbstractSystematic tree search augmented with constraint propagation has been regarded as the de facto standard approach to solve constraint satisfaction problems (CSPs). The property of completeness of tree search is superior to incomplete stochastic local search, although local search approach is more efficient in general. Many heuristics techniques have been developed to improve the efficiency of the tree search approach. In this paper, we propose a framework for combining and coordinating a complete tree search solver and a different solver in order to produce a complete and efficient CSP solver. Three different instances of the framework have been suggested including combining complete tree search with stochastic search, mathematical programming approach respectively. The experimental results show that this highly integrated hybrid scheme greatly improve the efficiency of constraint solving process in terms of both computation time and number of backtracking.
All Author(s) ListFung SKL, Zheng DJ, Leung HF, Lee JHM, Chun HW
Name of Conference16th IEEE International Conference on Tools with Artificial Intelligence
Start Date of Conference15/11/2004
End Date of Conference17/11/2004
Place of ConferenceBoca Raton
Country/Region of ConferenceUnited States of America
Journal name2011 23RD IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2011)
Detailed descriptioned. by Taghi M. Khoshgoftaar.
Year2004
Month1
Day1
PublisherIEEE COMPUTER SOC
Pages696 - 703
ISBN0-7695-2236-X
ISSN1082-3409
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence

Last updated on 2020-26-05 at 00:03