Developing a Declarative Rule Language for Applications in Product Configuration

  • Timo Soininen
  • Ilkka Niemelä
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1551)

Abstract

A rule-based language is proposed for product configuration applications. It is equipped with a declarative semantics providing formal definitions for main concepts in product configuration, including configuration models, requirements and valid configurations. The semantics uses Horn clause derivability to guarantee that each element in a configuration has a justification. This leads to favorable computational properties. For example, the validity of a configuration can be decided in linear time and other computational tasks remain in NP. It is shown that CSP and dynamic CSP can be embedded in the proposed language which seems to be more suitable for representing configuration knowledge. The rule language is closely related to normal logic programs with the stable model semantics. This connection is exploited in the first implementation which is based on a translator from rules to normal programs and on an existing high performance implementation of the stable model semantics, the Smodels system.

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References

  1. 1.
    T. Axling and S. Haridi. A tool for developing interactive configuration applications. Journal of Logic Programming, 19:658–679, 1994.Google Scholar
  2. 2.
    Y. Dimopoulos, B. Nebel, and J. Koehler. Encoding planning problems in nonmonotonic logic programs. In Proceedings of the Fourth European Conference on Planning. Springer-Verlag, 1997.Google Scholar
  3. 3.
    J. Dix. Semantics of logic programs: Their intuitions and formal properties. In Logic, Action and Information-Essays on Logic in Philosophy and Artificial Intelligence, pages 241–327. DeGruyter, 1995.Google Scholar
  4. 4.
    W.F. Dowling and J.H. Gallier. Linear-time algorithms for testing the satisfiability of propositional Horn formulae. Journal of Logic Programming, 3:267–284, 1984.CrossRefMathSciNetGoogle Scholar
  5. 5.
    T. Eiter and G. Gottlob. On the computational cost of disjunctive logic programming: Propositional case. Annals of Mathematics and Artificial Intelligence, 15:289–323, 1995.MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    M. Gelfond and V. Lifschitz. The stable model semantics for logic programming. In Proceedings of the 5th International Conference on Logic Programming, pages 1070–1080. The MIT Press, 1988.Google Scholar
  7. 7.
    A. Haselböck and M. Stumptner. An integrated approach for modelling complex configuration domains. In Proceedings of the 13th International Conference on Expert Systems, AI, and Natural Language, 1993.Google Scholar
  8. 8.
    R. Klein. A logic-based description of configuration: the constructive problem solving approach. In Configuration-Papers from the 1996 AAAI Fall Symposium. Technical Report FS-96-03, pages 111–118. AAAI Press, 1996.Google Scholar
  9. 9.
    X. Liu, C Ramakrishnan, and S. Smolka. Fully local and efficient evaluation of alternating fixed points. In Proceedings of 4th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, pages 5–19. Springer-Verlag, 1998.Google Scholar
  10. 10.
    J. McDermott. R1: a rule-based configurer of computer systems. Artificial Intelligence, 19(1):39–88, 1982.CrossRefGoogle Scholar
  11. 11.
    S. Mittal and B. Falkenhainer. Dynamic constraint satisfaction problems. In Proc. of the Eighth National Conference on Artificial Intelligence (AAAI-90), pages 25–32. AAAI, MIT Press, 1990.Google Scholar
  12. 12.
    I. Niemelä and P. Simons. Efficient implementation of the well-founded and stable model semantics. In Proceedings of the Joint International Conference and Symposium on Logic Programming, pages 289–303. The MIT Press, 1996.Google Scholar
  13. 13.
    I. Niemelä and P. Simons.Smodels-an implementation of the stable model and well-founded semantics for normal logic programs. In Proceedings of the 4th International Conference on Logic Programming and Non-Monotonic Reasoning, pages 420–429. Springer-Verlag, 1997.Google Scholar
  14. 14.
    C. Sakama and K. Inoue. An alternative approach to the semantics of disjunctive logic programs and deductive databases. Journal of Automated Reasoning, 13:145–172, 1994.MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    D. Searls and L. Norton. Logic-based configuration with a semantic network. Journal of Logic Programming, 8(1):53–73, 1990.CrossRefMATHGoogle Scholar
  16. 16.
    P. Simons. Towards constraint satisfaction through logic programs and the stable model semantics. Research report A47, Helsinki University of Technology, Helsinki, Finland, 1997. Available at http://saturn.hut.fi/pub/reports/A47.ps.gz.Google Scholar
  17. 17.
    T. Soininen and I. Niemelä. Formalizing configuration knowledge using rules with choices. Research report TKO-B142, Helsinki University of Technology, Helsinki, Finland, 1998. Presented at the Seventh International Workshop on Nonmonotonic Reasoning (NM’98), 1998.Google Scholar
  18. 18.
    J. Tiihonen, T. Soininen, T. Männistö, and R. Sulonen. State-of-the-practice in product configuration-a survey of 10 cases in the Finnish industry. In Knowledge Intensive CAD, volume 1, pages 95–114. Chapman & Hall, 1996.Google Scholar
  19. 19.
    E. Tsang. Foundations of Constraint Satisfaction. Academic Press, London, 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Timo Soininen
    • 1
  • Ilkka Niemelä
    • 2
  1. 1.TAI Research Center and Lab. of Information Processing ScienceHelsinki University of TechnologyHUTFinland
  2. 2.Dept. of Computer Science and Eng., Laboratory for Theoretical Computer ScienceHelsinki University of TechnologyHUTFinland

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