Efficient interpretation of prepositional multiple-valued logic programs

  • Gonzalo Escalada-Imaz
  • Felip Manyà
Part of the Lecture Notes in Computer Science book series (LNCS, volume 945)


Logic programming languages such as Prolog are widely used. A clear shortcoming of these languages is that every predicate can take only two truth values. A natural development is to consider that predicates could have many possible values. Thus, the main goal of this paper is to present an interpreter for infinitely-valued propositional logic programming. Some issues concerning the efficiency of the interpreter are discussed, and the negation as failure and the cut operator are also defined and integrated in the present multiple-valued context. The properties of the interpreter algorithm are carefully analyzed. Some of the areas of application of this work are expert systems and logic programming.


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  1. 1.
    Atanassov, K., and Georgiev, C. Intuitionistic fuzzy prolog. Fuzzy Sets and Systems 53 (1993), 121–129.Google Scholar
  2. 2.
    Baldwin, J. Evidential support logic programming. Fuzzy Sets and Systems 24 (1987), 1–26.Google Scholar
  3. 3.
    Béjar, R. Implementación de un intérprete proposicional y de un intérprete de primer orden para programación lógica multivaluada. EUP-Universitat de Lleida, 1993. (graduating project).Google Scholar
  4. 4.
    Dowling, W. F., and Gallier, J. H. Linear-time algorithms for testing the satisfiability of propositional horn formulae. Journal of Logic Programming 3 (1984), 267–284.Google Scholar
  5. 5.
    Dubois, D., Lang, J., and Prade, H. Poslog, an inference system based on possibilistic logic. Proceedings North American Fuzzy Information Processing Society Congress (1990), 177–180.Google Scholar
  6. 6.
    Escalada-Imaz, G.Optimisation d'Algorithmes d'Inference Monotone en Logique des Propositions et du Premier Ordre. Université Paul Sabatier, Toulouse, 1989. (PhD Thesis).Google Scholar
  7. 7.
    Escalada-Imaz, G., and Manyà, F. A linear interpreter for logic programming in multiple-valued propositional logic. In Proceedings of IPMU'94 (Paris, 1994), pp. 943–949.Google Scholar
  8. 8.
    Escalada-Imaz, G., and Manyà, F. Efficient Interpretation of Propositional Multiple-valued Logic Programs, IIIA Research Report 95-03, 1995.Google Scholar
  9. 9.
    Ghallab, M., and Escalada-Imaz, G. A linear control algorithm for a class of rule-based systems. Journal of Logic Programming 11 (1991), 117–132.Google Scholar
  10. 10.
    Godo, L.Contribució a l'Estudi de Models d'inferència en els Sistemes Possibilístics. FIB-UPC, Barcelona, 1990. (PhD Thesis).Google Scholar
  11. 11.
    Hähnle, R. Automated Deduction in Multiple-Valued Logics. Oxford University Press, 1993.Google Scholar
  12. 12.
    Lee, R. C. T. Fuzzy logic and the resolution principle. Journal of the Association for Computing Machinery 19, 1 (1972), 109–119.Google Scholar
  13. 13.
    Li, D., and Liu, G. A Fuzzy Prolog Database System. Research Studies Press and John Wiley and Sons, 1990.Google Scholar
  14. 14.
    Lloyd, J. W. Foundations of Logic Programming. Springer-Verlag, 1987.Google Scholar
  15. 15.
    Martin, T., Baldwin, J. F., and Pilsworth, B. W. The implementation of fpprolog: A fuzzy prolog interpreter. Fuzzy Sets and Systems 23 (1987), 119–129.Google Scholar
  16. 16.
    Mukaidono, M. Fundamentals of fuzzy prolog. International Journal of Aproximate Reasoning 3 (1989), 179–193.Google Scholar
  17. 17.
    Puyol, J., Godo, L., and Sierra, C. A specialisation calculus to improve expert systems communication. In ECAI'92 (Vienna, 1992 (extended version:IIIA Research Report 92/8), pp. 144–148.Google Scholar
  18. 18.
    Tamburrini, G., and Termini, S. Towards a resolution in a fuzzy logic with lukasiewicz implication. In Proceedings of IPMU'92 (Paris, 1992), pp. 271–277.Google Scholar
  19. 19.
    Trillas, E., and Valverde, L. On mode and implication in approximate reasoning. In Approximate Reasoning in Expert Systems, M. M. Gupta et al., Ed. North Holland, 1985.Google Scholar
  20. 20.
    Weigert, T. J., Tsai, J., and Liu, X. Fuzzy operator logic and fuzzy resolution. Journal of Automated Reasoning 10 (1993), 59–78.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Gonzalo Escalada-Imaz
    • 1
  • Felip Manyà
    • 1
    • 2
  1. 1.Institut d'Investigació en Intel.ligència Artificial (IIIA) Spanish Council for Scientific Research(CSIC)Campus Universität Autònoma de BarcelonaBellaterra, BarcelonaSpain
  2. 2.Departament d'Informàtica i Enginyeria IndustrialUniversitat de Lleida (UdL)LleidaSpain

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