Models and Tools for Improving Efficiency in Constraint Logic Programming

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DOI:

https://doi.org/10.7494/dmms.2011.5.1.69

Keywords:

Constraint Satisfaction Problem, Constraint Programming, Constraint Logic Programming

Abstract

Constraint Satisfaction Problems typically exhibit strong combinatorial explosion. In this paper we present some models and techniques aimed at improving efficiency in Constraint Logic Programming. A hypergraph model of constraints is presented and an outline of strategy planning approach focused on entropy minimization is put forward. An example cryptoaritmetic problem is explored in order to explain the proposed approach.

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References

Apt K.R. (2003). Principles of Constraint Programming. Cambridge University Press, Cambridge. DOI: http://doi.org/10.1017/CBO9780511615320.

Bratko I. (2000). Prolog Programming for Artificial Intelligence. 3rd ed., Addison-Wesley. DOI: http://doi.org/10.5555/357526.

Dechter R. (2003). Constraint Processing. Morgan Kaufmann Series in Artificial Intelligence, Morgan Kaufmann Publishers, San Francisco. DOI: http://doi.org/10.1016/B978-1-55860-890-0.X5000-2.

Ligęza A. (2009a). AND-OR graph with knowledge propagation rules as a model for constraint satisfaction problems. Automatyka, 13(2), pp. 411–419.

Ligęza A. (2009b). A constraint satisfaction framework for diagnostic problems. In: Z. Kowalczuk (Ed.). Diagnosis of Processes and Systems. Control and Computer Science: Information Technology, Control Theory, Fault and System Diagnosis, vol. 7, Pomeranian Science and Technology Publishers PWNT, Gdańsk, pp. 255–262.

Ligęza A. & Kościelny J.M. (2008). A new approach to multiple fault diagnosis: A combination of diagnostic matrices, graphs, algebraic and rule-based models. The case of two-layer models. International Journal of Applied Mathematics and Computer Science, 18(4), pp. 465–476. DOI: http://doi.org/10.2478/v10006-008-0041-8.

Russell S.J. & Norvig P. (2003). Artificial Intelligence: A Modern Approach. 2nd ed., Prentice Hall, Upper Saddle River, NJ. DOI: http://doi.org/10.5555/773294.

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Published

2011-10-03

Issue

Vol. 5 (2011): No. 1-2

Section

Articles

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Copyright (c) 2011 Decision Making in Manufacturing and Services

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Ligęza, A. (2011). Models and Tools for Improving Efficiency in Constraint Logic Programming. Decision Making in Manufacturing and Services, 5(1), 69-78. https://doi.org/10.7494/dmms.2011.5.1.69

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