Abstract
For a better treatment of Dynamic Constraint Satisfaction Problems (DCSPs), several techniques have been developed to be used in repair algorithms. We cite, for example, the variables/values ordering heuristics and local search techniques.
We distinguish between static heuristics, which calculate their values once at the beginning of the search, and dynamic heuristics that use an expensive intelligence in terms of solving time.
In this paper, we propose a new static variable ordering heuristic, Profound Degree (pdeg), based on deg heuristic, which calculates the degree of influence of a given variable, on the whole constraints network, relatively to its position in the network.
We evaluate this heuristic on the Extended Partial-order Dynamic Backtracking (EPBD) approach, which is an approach to repair DCSPs solutions, and we compare it to the best-known variables ordering heuristics (VOHs) for repairing. The evaluation of performance is on random binary problems and meeting scheduling problems, with the criteria of computation time, number of constraints checks and Hamming distance between the former and the current solution.
Chapter PDF
Similar content being viewed by others
References
Acodad, Y., Benelallam, I., Hammoujan, S., Bouyakhf, E.H.: Extended Partial-order Dynamic Backtracking algorithm for dynamically changed environments. In: 2012 IEEE 24th International Conference on (ICTAI), pp. 580–587 (November 7, 2012)
Ginsberg, M.L., McAllester, D.A.: Gsat and dynamic backtracking. Journal of Artificial Intelligence Research 1, 25–46 (1994)
Dechter, R., Dechter, A.: Belief maintenance in dynamic constraint networks. In: AAAI, pp. 37–42 (1988)
Bessiere, C.: Arc-consistency in dynamic constraint satisfaction problems. In: Proc. AAAI 1991, pp. 221–226. AAAI Press (1991)
Dechter, R., Meiri, I.: Experimental evaluation of preprocessing techniques in constraint satisfaction problems. In: Proceedings of IJCAI 1989, pp. 271–277 (1989)
Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: ECAI (August 2004)
Zivan, R., Alon, G., Amnon, M.: Hybrid search for minimal perturbation in Dynamic CSPs. Constraints 16(3), 228–249 (2011)
Hebrard, E., Barry, O., Walsh, T.: Distance Constraints in Constraint Satisfaction. In: IJCAI (January 6, 2007)
Ortiz-Bayliss, J., Terashima-Marin, H., Ender, O., Andrew, J., Santiago, E.: Exploring heuristic interactions in constraint satisfaction problems: A closer look at the hyper-heuristic space. In: 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE (June 20, 2013)
Bessiere, C., Jean-Charles, R.: MAC and combined heuristics: Two reasons to forsake FC (and CBJ?) on hard problems. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, pp. 61–75. Springer, Heidelberg (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 IFIP International Federation for Information Processing
About this paper
Cite this paper
Acodad, Y., Benamrane, A., Benelallam, I., Bouyakhf, E.H. (2014). Profound Degree: A Conservative Heuristic to Repair Dynamic CSPs. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds) Artificial Intelligence Applications and Innovations. AIAI 2014. IFIP Advances in Information and Communication Technology, vol 436. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44654-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-662-44654-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44653-9
Online ISBN: 978-3-662-44654-6
eBook Packages: Computer ScienceComputer Science (R0)