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Solving hard combinatorial problems with GSAT — A case study

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KI-96: Advances in Artificial Intelligence (KI 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1137))

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Abstract

In this paper, we investigate whether hard combinatorial problems such as the Hamiltonian circuit problem HCP (an NP-complete problem from graph theory) can be practically solved by transformation to the propositional satisfiability problem (SAT) and application of fast universal SAT-algorithms like GSAT to the transformed problem instances. By using the efficient transformation method proposed by Iwama and Miyazaki in 1994, one obtains a class of SAT-problems which are especially hard for SAT-algorithms based on local search such as GSAT. We identify structural properties of these problems which are responsible for GSAT's poor performance on this problem class. An empirical analysis indicates that several methods which have been designed to improve GSAT on structured problems are not effective for SAT-transformed HCP-instances.

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References

  1. A. Beringer, G. Aschemann, H.H. Hoos, M. Metzger, and A. Weiß. GSAT versus Simulated Annealing. In A. G. Cohn, editor, Proceedings of the European Conference on Artificial Intelligence, pages 130–134. John Wiley & Sons, 1994.

    Google Scholar 

  2. N. Boissin and J.-L. Lutton. A parallel simulated annealing algorithm. Parallel Computing, 19:859–872, 1993.

    Google Scholar 

  3. Byungki Cha and Kazuo Iwama. Performance test of local search algorithms using new types of random CNF formulas. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 304–310, 1995.

    Google Scholar 

  4. J.M. Crawford and L.D. Auton. Experimental results on the crossover point in satisfiability problems. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 21–27. MIT press, 1993.

    Google Scholar 

  5. Ian P. Gent and Toby Walsh. An empirical analysis of search in gsat. (Electronic) Journal of Artificial Intelligence Research, 1:47–59, 1993.

    Google Scholar 

  6. Ian P. Gent and Toby Walsh. Towards an understanding of hill-climbing procedures for SAT. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 28–33. MIT press, 1993.

    Google Scholar 

  7. Kazuo Iwama and Shuichi Miyazaki. SAT-variable complexity of hard combinatorial problems. In Proceedings of the World Computer Congress of the IFIP, pages 253–258 (Volume 1). Elsevier Science B.V., North Holland, 1994.

    Google Scholar 

  8. Kalev Kask and Rina Dechter. SAT and local consistency. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 612–622, 1995.

    Google Scholar 

  9. Kurt Konolige. Easy to be hard: difficult problems for greedy algorithms. In Proceedings of the International Conference on Principles of Knowlege Representation and Reasoning, pages 374–378, 1994.

    Google Scholar 

  10. Karl Rüdiger Reischuk. Einführung in die Komplexitätstheorie. Teubner Verlag, Stuttgart, 1990.

    Google Scholar 

  11. Bart Selman and Henry A. Kautz. Domain-independent extensions to GSAT: Solving large structured satisfiability problems. In R. Bajcsy, editor, Proceedings of the International Joint Conference on Artificial Intelligence, volume 1, pages 290–295. Morgan Kaufmann Publishers Inc., 1993.

    Google Scholar 

  12. Bart Selman and Henry A. Kautz. An empirical study of greedy local search for satisfiability testing. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 46–51. MIT press, 1993.

    Google Scholar 

  13. Bart Selman, Henry A. Kautz, and B. Cohen. Local search strategies for satisfiabiliy testing. Technical report, AT&T Bell Laboratories, Murray Hill, NJ, 1993.

    Google Scholar 

  14. Bart Selman, H. Levesque, and D. Mitchell. A new method for solving hard satisfiability problems. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 440–446. MIT press, 1992.

    Google Scholar 

  15. W.M. Spears. Simulated Annealing for hard satisfiability problems. Technical report, Naval Research Laboratory, Washington D.C., 1993.

    Google Scholar 

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Authors and Affiliations

  1. Fachgebiet Intellektik, Technische Hochschule Darmstadt, D-64289, Darmstadt, Germany

    Holger H. Hoos

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  1. Holger H. Hoos
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Günther Görz Steffen Hölldobler

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© 1996 Springer-Verlag Berlin Heidelberg

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Hoos, H.H. (1996). Solving hard combinatorial problems with GSAT — A case study. In: Görz, G., Hölldobler, S. (eds) KI-96: Advances in Artificial Intelligence. KI 1996. Lecture Notes in Computer Science, vol 1137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61708-6_53

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  • DOI: https://doi.org/10.1007/3-540-61708-6_53

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61708-2

  • Online ISBN: 978-3-540-70669-4

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