Abstract
Constraint Satisfaction problems (CSPs) are a fundamental concept used in many real world applications such as frequency assignment, configuration and conceptual design, scheduling and planning. A main challenge when designing a CSP-based system is the ability to deal with constraints in a dynamic and evolutive environment. During the conceptual phase of design, for example, the designers should be able to add/remove constraints at any time after specifying an initial statement describing the desired properties of a required artifact. We propose in this paper a new dynamic arc consistency algorithm that has a better compromise between time and space than those algorithms proposed in the literature, in addition to the simplicity of its implementation. Experimental tests on randomly generated CSPs demonstrate the efficiency of our algorithm to deal with large size problems in a dynamic environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mackworth, A.K.: Consistency in networks of relations. Artificial Intelligence 8, 99–118 (1977)
Haralick, R., Elliott, G.: Increasing tree search efficiency for Constraint Satisfaction Problems. Artificial Intelligence 14, 263–313 (1980)
Kumar, V.: Algorithms for constraint satisfaction problems: A survey. AI Magazine 13, 32–44 (1992)
Bessière, C.: Arc-consistency in dynamic constraint satisfaction problems. In: AAAI 1991, Anaheim, CA, pp. 221–226 (1991)
Debruyne, R.: Les algorithmes d’arc-consistance dans les csp dynamiques. Revue d’Intelligence Artificielle 9, 239–267 (1995)
Neuveu, B., Berlandier, P.: Maintaining arc consistency through constraint retraction. In: ICTAI 1994, pp. 426–431 (1994)
Mohr, R., Henderson, T.: Arc and path consistency revisited. Artificial Intelligence 28, 225–233 (1986)
Bessière, C.: Arc-consistency and arc-consistency again. Artificial Intelligence 65, 179–190 (1994)
Bessière, C., Freuder, E., Regin, J.: Using inference to reduce arc consistency computation. In: IJCAI 1995, Montréal, Canada, pp. 592–598 (1995)
Wallace, R.J.: Why AC-3 is almost always better than AC-4 for establishing arc consistency in CSPs. In: IJCAI 1993, Chambery, France, pp. 239–245 (1993)
Neuveu, B., Berlandier, P.: Arc-consistency for dynamic constraint satisfaction problems: An rms free approach. In: ECAI 1994, Workshop on Constraint Satisfaction Issues Raised by Practical Applications, Amsterdam (1994)
Zhang, Y., Yap, R.H.C.: Making ac-3 an optimal algorithm. In: Seventeenth International Joint Conference on Artificial Intelligence (IJCAI 2001), Seattle, WA, pp. 316–321 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mouhoub, M. (2004). Solving Dynamic CSPs. In: Tawfik, A.Y., Goodwin, S.D. (eds) Advances in Artificial Intelligence. Canadian AI 2004. Lecture Notes in Computer Science(), vol 3060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24840-8_45
Download citation
DOI: https://doi.org/10.1007/978-3-540-24840-8_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22004-6
Online ISBN: 978-3-540-24840-8
eBook Packages: Springer Book Archive