Skip to main content

A Declarative Semantics for a Fuzzy Logic Language Managing Similarities and Truth Degrees

  • Conference paper
  • First Online:
Rule Technologies. Research, Tools, and Applications (RuleML 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9718))

Abstract

This work proposes a declarative semantics based on a fuzzy variant of the classical notion of least Herbrand model for the so-called FASILL language (acronym of “Fuzzy Aggregators and Similarity Into a Logic Language”) which has been recently designed and implemented in our research group for coping with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity.

Work supported by the EU (FEDER), and the Spanish MINECO Ministry (Ministerio de Economía y Competitividad) under grant TIN2013-45732-C4-2-P.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Two different programming environments for \(\mathsf{Bousi}{\sim }\mathsf{Prolog}\) are available at http://dectau.uclm.es/bousi/.

  2. 2.

    The tool is freely accessible from the Web site http://dectau.uclm.es/floper/.

  3. 3.

    This convention is quite standard and even used in a pure logic language like Prolog, where the reserved words true and fail -which directly resemble the pair of elements conforming the fixed lattice of truth degrees associated to any Prolog program- can be freely used on goals and clause bodies.

  4. 4.

    Here, the connectives \(\varsigma \) are binary operations but we usually generalize them with an arbitrary number of arguments, that is, with truth function \(\mathbf {\dot{\varsigma }} : L^n\rightarrow L\).

  5. 5.

    Note that, in the antecedent of this implication we use the order for pairs, (which is defined as \((x_1,y_1)\le (x_2,y_2)\) if, and only if, \(x_1\le x_2 \text{ and } y_1\le y_2\)), while in the consequent the usual order on the interval [0, 1] is considered. Similarly, it is possible to extend the usual order on [0, 1], for n-ary connectives.

  6. 6.

    For convenience, \({\mathcal R}(x,y)\), also denoted \(x{\mathcal R}y\), refers to both the syntactic expression (that symbolizes that the elements \(x,y\in {\mathcal U}\) are related by \({\mathcal R}\)) and the membership degree \(\mu _{\mathcal R}(x,y)\), i.e., the affinity degree of the pair \((x,y)\in {\mathcal U}\times {\mathcal U}\) with the verbal predicate (or fuzzy predicate) \({\mathcal R}\).

  7. 7.

    Note that elegant(taxi) and vanguardist(metro) are 1-ary predicates, whereas that taximetro are terms with arity 0, i.e. constants.

  8. 8.

    Note that, X-MALP programs do not rely on adjoint pairs.

  9. 9.

    Sometimes we will abbreviate writing “fuzzy model” or simply “model”.

  10. 10.

    Sometimes we will abbreviate writing “least fuzzy model” or simply “least model”.

  11. 11.

    The last version of the FLOPER system which copes with similarity relations can be freely downloaded from http://dectau.uclm.es/floper/?q=sim and it can be tested on-line through http://dectau.uclm.es/floper/?q=sim/test.

References

  1. Arcelli, F.: Likelog for flexible query answering. Soft Comput. 7(2), 107–114 (2002)

    Article  MATH  Google Scholar 

  2. Arcelli, F., Formato, F.: Likelog: a logic programming language for flexible data retrieval. In: Proceedings of the ACM Symposium on Applied Computing, SAC 1999, San Antonio, Texas, pp. 260–267. ACM, Artificial Intelligence and Computational Logic (1999)

    Google Scholar 

  3. Caballero, R., Rodríguez-Artalejo, M., Romero-Díaz, C.A.: Similarity-based reasoning in qualified logic programming. In: Proceedings of the 10th International ACM SIGPLAN Conference on Principles and Practice of Declarative Programming, PPDP 2008, pp. 185–194. ACM, New York (2008)

    Google Scholar 

  4. Caballero, R., Rodríguez-Artalejo, M., Romero-Díaz, C.A.: A transformation-based implementation for CLP with qualification and proximity. Theory Pract. Logic Program. 14(1), 1–63 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  5. Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York (1980)

    MATH  Google Scholar 

  6. Formato, F., Gerla, G., Sessa, M.I.: Similarity-based unification. Fundamenta Informaticae 41(4), 393–414 (2000)

    MathSciNet  MATH  Google Scholar 

  7. Julián Iranzo, P., Moreno, G., Penabad, J., Vázquez, C.: A fuzzy logic programming environment for managing similarity and truth degrees. In: Escobar, S. (ed.) Proceedings of XIV Jornadas sobre Programación y Lenguajes, PROLE 2014, Cádiz, Spain. EPTCS, vol. 173, pp. 71–86 (2015). http://dx.doi.org/10.4204/EPTCS.173.6

  8. Julián, P., Moreno, G., Penabad, J.: On fuzzy unfolding. A multi-adjoint approach. Fuzzy Sets Syst. 154, 16–33 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. Julián, P., Moreno, G., Penabad, J.: On the declarative semantics of multi-adjoint logic programs. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009, Part I. LNCS, vol. 5517, pp. 253–260. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  10. 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, Heidelberg (2015)

    Chapter  Google Scholar 

  11. Julián-Iranzo, P., Rubio-Manzano, C.: A declarative semantics for Bousi\(\sim \)Prolog. In: Proceedings of 11th International ACM SIGPLAN Conference on Principles and Practice of Declarative Programming, PPDP 2009, Coimbra, Portugal, pp. 149–160. ACM (2009)

    Google Scholar 

  12. Julián-Iranzo, P., Rubio-Manzano, C.: An efficient fuzzy unification method and its implementation into the Bousi\(\sim \)Prolog system. In: Proceedings of the 2010 IEEE International Conference on Fuzzy Systems, Barcelona, Spain, pp. 1–8 (2010). http://dx.doi.org/10.1109/FUZZY.2010.5584193

  13. Kifer, M., Subrahmanian, V.S.: Theory of generalized annotated logic programming and its applications. J. Logic Program. 12, 335–367 (1992)

    Article  MathSciNet  Google Scholar 

  14. Lloyd, J.W.: Foundations of Logic Programming. Springer, Berlin (1987)

    Book  MATH  Google Scholar 

  15. Medina, J., Ojeda-Aciego, M., Vojtáš, P.: Similarity-based unification: a multi-adjoint approach. Fuzzy Sets Syst. 146, 43–62 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  16. Morcillo, P.J., Moreno, G., Penabad, J., Vázquez, C.: A practical management of fuzzy truth-degrees using FLOPER. In: Dean, M., Hall, J., Rotolo, A., Tabet, S. (eds.) RuleML 2010. LNCS, vol. 6403, pp. 20–34. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  17. Moreno, G., Penabad, J., Vázquez, C.: Beyond multi-adjoint logic programming. Int. J. Comput. Math. 92(9), 1956–1975 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  18. Moreno, G., Vázquez, C.: Fuzzy logic programming in action with FLOPER. J. Softw. Eng. Appl. 7, 237–298 (2014)

    Article  Google Scholar 

  19. Muñoz-Hernández, S., Ceruelo, V.P., Strass, H.: RFuzzy: Syntax, semantics and implementation details of a simple and expressive fuzzy tool over Prolog. Inform. Sci. 181(10), 1951–1970 (2011)

    Article  MathSciNet  Google Scholar 

  20. Nguyen, H.T., Walker, E.A.: A First Course in Fuzzy Logic. Chapman & Hall, Boca Ratón (2006)

    MATH  Google Scholar 

  21. 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)

    Chapter  Google Scholar 

  22. Rubio-Manzano, C., Julián-Iranzo, P.: A fuzzy linguistic prolog and its applications. J. Intell. Fuzzy Syst. 26(3), 1503–1516 (2014)

    Google Scholar 

  23. Sessa, M.I.: Translations and similarity-based logic programming. Soft Comput. 5(2), 160–170 (2001). http://dx.doi.org/10.1007/PL00009891

    Google Scholar 

  24. Sessa, M.I.: Approximate reasoning by similarity-based SLD resolution. Theoret. Comput. Sci. 275(1–2), 389–426 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  25. van Emden, M.H.: Quantitative deduction and its fixpoint theory. J. Logic Program. 3(1), 37–53 (1986)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ginés Moreno .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Julián-Iranzo, P., Moreno, G., Penabad, J., Vázquez, C. (2016). A Declarative Semantics for a Fuzzy Logic Language Managing Similarities and Truth Degrees. In: Alferes, J., Bertossi, L., Governatori, G., Fodor, P., Roman, D. (eds) Rule Technologies. Research, Tools, and Applications. RuleML 2016. Lecture Notes in Computer Science(), vol 9718. Springer, Cham. https://doi.org/10.1007/978-3-319-42019-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42019-6_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42018-9

  • Online ISBN: 978-3-319-42019-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics