An Online Tool for Tuning Fuzzy Logic Programs

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10364)

Abstract

In this paper we are concerned with a fuzzy logic language where program rules extend the classical notion of clause by adding fuzzy connectives and truth degrees on their bodies. In this work we describe an efficient online tool which helps to select such operators and weights in an automatic way, accomplishing with our recent technique for tuning this kind of fuzzy programs. The system offers a comfortable interaction with users for introducing test cases and also provides useful information about the choices that better fit their preferences.

Keywords

Fuzzy logic programming Symbolic execution Tuning 

References

  1. 1.
    Almendros-Jiménez, J.M., Luna, A., Moreno, G.: Fuzzy xpath through fuzzy logic programming. New Generation Comput. 33(2), 173–209 (2015)CrossRefGoogle Scholar
  2. 2.
    Baldwin, J.F., Martin, T.P., Pilsworth, B.W.: Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence. Wiley, New York (1995)Google Scholar
  3. 3.
    Guadarrama, S., Muñoz, S., Vaucheret, C.: Fuzzy Prolog: a new approach using soft constraints propagation. Fuzzy Sets Syst. 144(1), 127–150 (2004)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Julián Iranzo, P., Moreno, G., Penabad, J., Vázquez, C.: A fuzzy logic programming environment for managing similarity and truth degrees. In: Escobar, S. (ed.), Proceeding of XIV Jornadas sobre Programación y Lenguajes, PROLE 2014, Cádiz, Spain, vol. 173. EPTCS, pp. 71–86 (2015). doi: 10.4204/EPTCS.173.6
  5. 5.
    Julián-Iranzo, P., Moreno, G., Penabad, J., Vázquez, C.: A declarative semantics for a fuzzy logic language managing similarities and truth degrees. In: Alferes, J.J.J., Bertossi, L., Governatori, G., Fodor, P., Roman, D. (eds.) RuleML 2016. LNCS, vol. 9718, pp. 68–82. Springer, Cham (2016). doi: 10.1007/978-3-319-42019-6_5 CrossRefGoogle Scholar
  6. 6.
    Julián-Iranzo, P., Moreno, G., Vázquez, C.: Similarity-based strict equality in a fully integrated fuzzy logic language. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 193–207. Springer, Cham (2015). doi: 10.1007/978-3-319-21542-6_13 CrossRefGoogle Scholar
  7. 7.
    Ishizuka, M., Kanai, N.: Prolog-ELF incorporating fuzzy logic. In: Proceeding of the 9th International Joint Conference on Artificial Intelligence, IJCAI 1985, pp. 701–703. Morgan Kaufmann (1985)Google Scholar
  8. 8.
    Julián, P., Medina, J., Moreno, G., Ojeda, M.: Efficient thresholded tabulation for fuzzy query answering. In: Bouchon-Meunier, B., Magdalena, L., Ojeda-Aciego, M., Verdegay, J.-L., Yager, R.R. (eds.) Foundations of Reasoning under Uncertainty. STUDFUZZ, vol. 249, pp. 125–141. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-10728-3_7 CrossRefGoogle Scholar
  9. 9.
    Julián, P., Moreno, G., Penabad, J.: An improved reductant calculus using fuzzy partial evaluation techniques. Fuzzy Sets Syst. 160, 162–181 (2009). doi: 10.1016/j.fss.2008.05.006 MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Kifer, M., Subrahmanian, V.S.: Theory of generalized annotated logic programming and its applications. J. Logic Programm. 12, 335–367 (1992)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Lassez, J.L., Maher, M.J., Marriott, K.: Unification revisited. In: Foundations of Deductive Databases and Logic Programming, pp. 587–625. Morgan Kaufmann, Los Altos (1988)Google Scholar
  12. 12.
    Lee, R.C.T.: Fuzzy logic and the resolution principle. J. ACM 19(1), 119–129 (1972)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Li, D., Liu, D.: A Fuzzy Prolog Database System. Wiley, New York (1990)Google Scholar
  14. 14.
    Lloyd, J.W.: Foundations of Logic Programming. Springer, Berlin (1987)CrossRefMATHGoogle Scholar
  15. 15.
    Medina, J., Ojeda-Aciego, M., Vojtáš, P.: Similarity-based unification: a multi-adjoint approach. Fuzzy Sets Syst. 146, 43–62 (2004)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Moreno, G., Penabad, J., Vidal, G.: Tuning fuzzy logic programs with symbolic execution. CoRR, abs/1608.04688 (2016)Google Scholar
  17. 17.
    Nguyen, H.T., Walker, E.A.: A First Course in Fuzzy Logic. Chapman & Hall, Boca Ratón (2006)MATHGoogle Scholar
  18. 18.
    Rodríguez-Artalejo, M., Romero-Díaz, C.A.: Quantitative logic programming revisited. In: Garrigue, J., Hermenegildo, M.V. (eds.) FLOPS 2008. LNCS, vol. 4989, pp. 272–288. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-78969-7_20 CrossRefGoogle Scholar
  19. 19.
    Straccia, U.: Managing uncertainty and vagueness in description logics, logic programs and description logic programs. In: Baroglio, C., Bonatti, P.A., Maluszynski, J., Marchiori, M., Polleres, A., Schaffert, S. (eds.) Reasoning Web. LNCS, vol. 5224, pp. 54–103. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-85658-0_2 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computing SystemUniversity of Castilla-La ManchaAlbaceteSpain

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