Skip to main content
Log in

Proof of Theorems in Fuzzy Logic Based on Structural Resolution

  • Published:
Cybernetics and Systems Analysis Aims and scope

Abstract

An approach to proving theorems with fuzzy and not quite true argumentation is considered. Zadeh’s compositional rule of inference is used as the rule of provably correct reasoning, and its procedural implementation is enabled by a refutation mechanism. As such a mechanism, structural resolution (S-resolution) is proposed that is a generalization of the principle of resolutions to fuzzy statements. S-resolution is based on semantic indices of letters and their similarity. Semantic indices are essential in S-resolution. They contain data used as control information in the process of inference. And similarity implies finding letters to obtain an S-resolvent. Combining Zadeh’s compositional rule of inference and S-resolution allows, on the one hand, to withdraw the problem of correctness of resolvents in fuzzy logic and, on the other hand, to ensure the regularity of the process of a proof in two-valued and fuzzy logics.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. M. Mukaidono, “Fuzzy deduction of resolution type,” in: R. R. Yager (ed.), Fuzzy Sets and Possibility Theory. Recent Developments [Russian translation], Radio i Sv’yaz, Moscow (1986), pp. 153–161.

  2. D. Dubois and H. Prade, “Necessity and the resolution principle,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 17, No. 3, 474–478 (1987).

  3. C. S. Kim, D. S. Kim, and J. Park, “A new fuzzy resolution principle based on the antonym,” Fuzzy Sets and Systems, Vol. 113, No. 2, 299–307 (2000).

  4. F. A. Fontana and F. Formato, “A similarity-based resolution principle,” International Journal of Intelligent Systems, Vol. 17, No. 9, 853–872 (2002).

  5. S. Raha and K. S. Ray, “Approximate reasoning based on generalised disjunctive syllogism,” Fuzzy Sets and Systems, Vol. 61, No. 2, 143–151 (1994).

  6. H. Habiballa, “Resolution principle in fuzzy predicate logic,” Acta Fac. Paed. Univ. Tyrnaviensis, Ser. C, No. 9 3–12 (2005).

  7. H. Habiballa, “Resolution principle and fuzzy logic,” in: E. Dadios (ed.), Fuzzy Logic — Algorithms, Techniques, and Implementations, Ch. 3, IntechOpen, London (2012), pp. 55–74.

  8. S. D. Shtovba, Design of Fuzzy Systems by Means of MATLAB [in Russian], Hot Line – Telecom, Moscow (2007).

  9. S. Raha, N. R. Pal, and K. S. Ray, “Similarity based approximate reasoning: Methodology and application,” IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Vol. 32, No. 4. 541–547 (2002).

  10. B. Mondal, D. Mazumdar, and S. Raha, “Similarity in approximate reasoning,” International Journal of Computational Cognition, Vol. 4, No. 3, 46–56 (2006).

  11. Mondal B., Raha S. Similarity-based inverse approximate reasoning. IEEE Transaction on Fuzzy Systems, Vol. 19, No. 6. 1058–1071 (2011).

  12. B. Mondal and S. Raha, “Approximate reasoning in fuzzy resolution,” International Journal of Intelligence Science. Vol. 3, No 2, 86–98 (2013).

  13. Yu. Ya. Samokhvalov, “Problem-oriented theorem-proving method in fuzzy logic (PO-method),” Cybernetics and Systems Analysis, Vol. 31, No. 5, 682–690 (1995).

  14. L.A. Zadeh, The Concept of a Linguistic Variable and Its Application to Making Approximate Reasoning [Russian translation], Mir, Moscow (1976).

  15. M. Bofill, G. Moreno, C. Vázquez, and M. Villaret, “Automatic proving of fuzzy formulae with fuzzy logic programming and SMT,” in: Actas de las XIII Jornadas sobre Programacio'n y Lenguajes, PROLE’13, Jornadas SISTEDES, Madrid (2013), pp, 151–165.

  16. Yu. Ya. Samokhvalov, “The Assessment of the administrative decisions validity by fuzzy logic,” USiM, No. 3, 26–34 (2017).

  17. Yu. Ya. Samokhvalov, “Coordination of expert estimates in matrices of preference relations,” USiM, No. 6, 49–53 (2002).

  18. Zwick R., Carlstein E., Budescu D.V. Measures of similarity among fuzzy concepts: A comparative analysis. International Journal of Approximat., Vol. 1, No. 2, 221–242 (1987).

  19. B. Mondal, D. Mazumdar, and S. Raha “Similarity in approximate reasoning,” International Journal of Computational Cognition, Vol. 4, No. 3. 46–56 (2006).

  20. B. M. Wilamowski and J. D. Irwin (eds.), The Industrial Electronics Handbook: Intelligent Systems, CRC Press, Boca Raton (2011).

  21. Yu. Ya. Samokhvalov, “Automatic theorem proving and fuzzy situational search for decisions,” Cybernetics and Systems Analysis, Vol. 37, No. 4, 509–514 (2001).

  22. M. Ford, The Rise of the Robots: Technology and the Threat of Jobless Future, Basic Books, New York (2015).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yu. Ya. Samokhvalov.

Additional information

Translated from Kibernetika i Sistemnyi Analiz, No. 2, March–April, 2019, pp. 44–58.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Samokhvalov, Y.Y. Proof of Theorems in Fuzzy Logic Based on Structural Resolution. Cybern Syst Anal 55, 207–219 (2019). https://doi.org/10.1007/s10559-019-00125-8

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10559-019-00125-8

Keywords

Navigation