Assisting Domain Experts to Formulate and Solve Constraint Satisfaction Problems

  • Derek Sleeman
  • Stuart Chalmers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4248)


Constraint satisfaction is a powerful approach to solving a wide class of problems. However, as many non-experts have difficulties formulating tasks as Constraint Satisfaction Problems (CSPs), we have built a number of interfaces for particular kinds of CSPs, including crypt-arithmetic problems, map-colouring problems, and scheduling tasks, which ask highly focused questions of the user, c.f., the earlier MOLE/MORE, and SALT knowledge acquisition systems. Information from each of these interfaces is then transformed initially into a structured format which is semantic web compliant and is secondly transformed into the format required by the generic constraint satisfaction problem solver. When this problem solver is run, the user is either provided with solution(s) or feedback that the problem is underspecified (when many solutions are feasible) or over-specified (when no solution is possible). The system has 3 distinct phases, namely; information capture, transformation of the information to that used by a standard problem solver, and thirdly the solving and user feedback phase.


Knowledge Acquisition Constraint Satisfaction Constraint Satisfaction Problem Constraint Satisfaction Problem Instance Solve Constraint Satisfaction Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fish, A., Flower, J.: Investigating reasoning with constraint diagrams. In: VLFM 2004, Visual Languages and Formal Methods, vol. 127, pp. 53–69. Elsevier, Rome (2005)Google Scholar
  2. 2.
    Eshelman, L.: Mole: a knowledge-acquisition tool for cover-and-differentiate systems. In: Marcus, S. (ed.) Automating Knowledge Acquisition for Expert Systems, pp. 37–80. Kluwer Academic, Norwood (1988)Google Scholar
  3. 3.
    Freuder, E.C.: Partial Constraint Satisfaction. In: Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-1989, Detroit, Michigan, USA, pp. 278–283 (1989)Google Scholar
  4. 4.
    Frisch, A.M., Grum, M., Jefferson, C., Martinez-Hernandez, B., Miguel, I.: The essence of essence: A constraint language for specifying combinatorial problems. In: Proceedings of the 4th International Workshop on Modelling and Reformulating Constraint Satisfaction Problems, pp. 73–88 (2005)Google Scholar
  5. 5.
    Kahn, G.: More: From observing knowledge engineers to automating knowledge acquisition. In: Marcus, S. (ed.) Automating Knowledge acquisition for Expert Systems, pp. 7–35. Kluwer Academic, Dordrecht (1988)Google Scholar
  6. 6.
    Marcus, S., McDermott, J.: Salt: a knowledge acquisition language for propose-and-revise systems. Artif. Intell. 39(1), 1–37 (1989)zbMATHCrossRefGoogle Scholar
  7. 7.
    Renker, G.: A modeling framework for constraints. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, p. 773. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Smith, B.: A tutorial on constraint programming. Technical Report 95.14, School of Computing Research Report, University of Leeds (April 1995)Google Scholar
  9. 9.
    Zhang, Y., Vasconcelos, W., Sleeman, D.: Ontosearch: An ontology search engine. In: The Twenty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Derek Sleeman
    • 1
  • Stuart Chalmers
    • 1
  1. 1.Department of Computing ScienceUniversity of AberdeenAberdeenScotland, UK

Personalised recommendations