Published in Volume XVII, 2007, pages 83-112

Authors: C. Frăsinaru


Many computationally difficult problems from areas like planning and scheduling are easily modelled as constraint satisfaction problems (CSP). In order to have an uniform practical approach of these, a new programming paradigm emerged in the form of constraint programming, providing the opportunity of having declarative descriptions of CSP instances and also obtaining their solutions in reasonable computational time.

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[1] K. Apt. Principles of constraint programming. Cambridge University Press, 2003.

[2] R. Bartak. On-line guide to constraint programming.

[3] S. Bistarelli. Semirings for Soft Constraint Solving and Programming. Springer, 2004.

[4] Y. Caseau and F. Laburthe. Palm.

[5] R. Dechter. Constraint Processing. Morgan Kaufmann, 2003.

[6] E. C. Freuder. Synthesizing constraint expressions. Communications of the ACM, 21(11):958-966, 1978.

[7] E. C. Freuder. Partial constraint satisfaction. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89, Detroit, Michigan, USA, pages 278-283, 1989.

[8] C. Frasinaru. Omnics.

[9] C. Frasinaru. Curs practic de Java. Matrix Rom Bucuresti, 2005.

[10] E. Gamma, R. Helm, R. Johnson, and J. Vlissides. Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley Professional, 1995.

[11] M. Gavanelli, E. Lamma, P. Mello, and M. Milano. Performance measurement of interactive CSP search algorithms. In Giornata di Lavoro RCRA su “Analisi sperimentale di algoritmi per l’Intelligenza Artificiale”, Rome, Italy, Dec 16 1999.

[12] Ilog. Ilog solver.

[13] Koalog. Koalog constraint solver tutorial.

[14] F. Laburthe and N. Jussien. Choco.

[15] E. Lamma, P. Mello, M. Milano, R. Cucchiara, M. Gavanelli, and M. Piccardi. Constraint propagation and value acquisition: Why we should do it interactively. In IJCAI, pages 468-477, 1999.

[16] J.-L. Lauriere. A language and a program for stating and solving combinatorial problems. Artificial Intelligence. An International Journal, 10(1):29-127, 1978.

[17] A. K. Mackworth. Consistency in networks of relations. Artificial Intelligence, 8(1):99-118, 1977.

[18] U. Monatanari. Networks of constraints: Fundamental properties and application to picture processing. Information Science, 7(2):95-132, 1974.

[19] P. Boizumault N. Jussien, R.Debruyne. Mantaining arc-consistency within dynamic backtracking. In Sixth international conference on principles and practice of constraint programming (CP’2000), 2000.

[20] J.-C. Regin. Global constraints. First International Summer School on Constraint Programming Acquafredda di Maratea – Italy, September 11-15 2005.

[21] F. Rossi, P. van Beek, and T. Walsh (editors). Handbook of Constraint Programming. Elsevier, 2006.

[22] T. Schiex. Soft constraint processing. First International Summer School on Constraint Programming, July 2005.

[23] M. Sergot. A query-the-user facility of logic programming. In In Degano, P., Sandwell, E., eds.: Integrated Interactive Computer Systems, North Holland, pages 27-41, 1983.

[24] G. Verfaille, T. Schiex, H. Fargier. Valued constrained satisfaction problems: hard and easy problems. Proc. of the 14th IJCAI, pages 631-637, 1995.

[25] E. Tsang. Foundations of Constraint Satisfaction. Academic Press, 1993.

[26] J. W.Cooper. The Design Patterns Java Companion. Addison-Wesley, 1998


  title={Basic Techniques for Creating an Efficient CSP Solver},
  author={C. Fr{u a}sinaru},
  journal={Scientific Annals of Computer Science},
  organization={``A.I. Cuza'' University, Iasi, Romania},
  publisher={``A.I. Cuza'' University Press}