An Online Tool for Unfolding Symbolic Fuzzy Logic Programs

  • Ginés MorenoEmail author
  • José Antonio Riaza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11507)


In many declarative frameworks, unfolding is a very well-known semantics-preserving transformation technique based on the application of computational steps on the bodies of program rules for improving efficiency. In this paper we describe an online tool which allows us to unfold a symbolic extension of a modern fuzzy logic language where program rules can embed concrete and/or symbolic fuzzy connectives and truth degrees on their bodies. The system offers a comfortable interaction with users for unfolding symbolic programs and it also provides useful options to navigate along the sequence of unfolded programs. Finally, the symbolic unfolding transformation is connected with some fuzzy tuning techniques that we previously implemented on the same tool.


Fuzzy logic programming Symbolic execution Unfolding 


  1. 1.
    Baldwin, J.F., Martin, T.P., Pilsworth, B.W.: Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence. Wiley, Hoboken (1995)Google Scholar
  2. 2.
    Guadarrama, S., Muñoz, S., Vaucheret, C.: Fuzzy prolog: a new approach using soft constraints propagation. Fuzzy Sets Syst. 144(1), 127–150 (2004)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Ishizuka, M., Kanai, N.: Prolog-ELF incorporating fuzzy logic. In: Joshi, A.K. (ed.) Proceedings of the 9th International Joint Conference on Artificial Intelligence, IJCAI 1985, pp. 701–703. Morgan Kaufmann (1985)Google Scholar
  4. 4.
    Julián-Iranzo, P., Medina-Moreno, J., Morcillo, P.J., Moreno, G., Ojeda-Aciego, M.: An unfolding-based preprocess for reinforcing thresholds in fuzzy tabulation. In: Rojas, I., Joya, G., Gabestany, J. (eds.) IWANN 2013. LNCS, vol. 7902, pp. 647–655. Springer, Heidelberg (2013). Scholar
  5. 5.
    Julián-Iranzo, P., Moreno, G., Penabad, J.: On fuzzy unfolding. A multi-adjoint approach. Fuzzy Sets Syst. 154, 16–33 (2005)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Julián-Iranzo, P., Moreno, G., Penabad, J.: Thresholded semantic framework for a fully integrated fuzzy logic language. J. Log. Algebr. Methods Program. 93, 42–67 (2017)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Lassez, J.L., Maher, M.J., Marriott, K.: Unification revisited. In: Minker, J. (ed.) Foundations of Deductive Databases and Logic Programming, pp. 587–625. Morgan Kaufmann, Los Altos (1988)CrossRefGoogle Scholar
  8. 8.
    Lee, R.C.T.: Fuzzy logic and the resolution principle. J. ACM 19(1), 119–129 (1972)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Li, D., Liu, D.: A Fuzzy Prolog Database System. Wiley, Hoboken (1990)Google Scholar
  10. 10.
    Medina, J., Ojeda-Aciego, M., Vojtáš, P.: Similarity-based unification: a multi-adjoint approach. Fuzzy Sets Syst. 146, 43–62 (2004)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Morcillo, P.J., Moreno, G.: Improving multi-adjoint logic programs by unfolding fuzzy connective definitions. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2015. LNCS, vol. 9094, pp. 511–524. Springer, Cham (2015). Scholar
  12. 12.
    Moreno, G., Penabad, J., Riaza, J.A.: On similarity-based unfolding. In: Moral, S., Pivert, O., Sánchez, D., Marín, N. (eds.) SUM 2017. LNCS (LNAI), vol. 10564, pp. 420–426. Springer, Cham (2017). Scholar
  13. 13.
    Moreno G., Penabad J., Riaza J.A.: Symbolic unfolding of multi-adjoint logic programs. In: 9th European Symposium on Computational Intelligence and Mathematics, ESCIM 2017, pp. 1–8 (2017). (extended version published by Springer)
  14. 14.
    Moreno, G., Penabad, J., Riaza, J.A.: Symbolic unfolding of multi-adjoint logic programs. In: Cornejo, M.E., Kóczy, L.T., Medina, J., De Barros Ruano, A.E. (eds.) Trends in Mathematics and Computational Intelligence. SCI, vol. 796, pp. 43–51. Springer, Cham (2019). Scholar
  15. 15.
    Moreno, G., Penabad, J., Riaza, J.A., Vidal, G.: Symbolic execution and thresholding for efficiently tuning fuzzy logic programs. In: Hermenegildo, M.V., Lopez-Garcia, P. (eds.) LOPSTR 2016. LNCS, vol. 10184, pp. 131–147. Springer, Cham (2017). Scholar
  16. 16.
    Moreno, G., Riaza, J.A.: An online tool for tuning fuzzy logic programs. In: Costantini, S., Franconi, E., Van Woensel, W., Kontchakov, R., Sadri, F., Roman, D. (eds.) RuleML+RR 2017. LNCS, vol. 10364, pp. 184–198. Springer, Cham (2017). Scholar
  17. 17.
    Nguyen, H.T., Walker, E.A.: A First Course in Fuzzy Logic. Chapman & Hall, Boca Ratón (2006)zbMATHGoogle Scholar
  18. 18.
    Pettorossi, A., Proietti, M.: Rules and strategies for transforming functional and logic programs. ACM Comput. Surv. 28(2), 360–414 (1996)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computing SystemsUCLMAlbaceteSpain

Personalised recommendations