Skip to main content

An Approach to Inconsistency-Tolerant Reasoning About Probability Based on Łukasiewicz Logic

  • Conference paper
  • First Online:
Scalable Uncertainty Management (SUM 2022)

Abstract

In this paper we consider the probability logic over Łukasiewicz logic with rational truth-constants, denoted FP(RPL), and we explore a possible approach to reason from inconsistent FP(RPL) theories in a non-trivial way. It basically consists of suitably weakening the formulas in an inconsistent theory T, depending on the degree of inconsistency of T. We show that such a logical approach is in accordance with other proposals in the literature based on distance-based and violation-based inconsistency measures.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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.

    Although we are using the same symbols \(\wedge , \lnot , \vee , \rightarrow \) as in Łukasiewicz logic to denote the conjunction, negation, disjunction and implication, the context will help in avoiding any confusion. In particular classical logic connectives will appear only under the scope of the operator P, see below.

  2. 2.

    An equivalent formulation of (FP3) is \(P(\varphi \vee \psi ) \equiv P\varphi \oplus (P\psi \ominus P(\varphi \wedge \psi ))\).

References

  1. Baldi, P., Cintula, P., Noguera, C.: Classical and fuzzy two-layered modal logics for uncertainty: translations and proof-theory. Int. J. Comput. Intell. Syst. 13(1), 988–1001 (2020)

    Article  Google Scholar 

  2. Bertossi, L., Hunter, A., Schaub, T. (eds.): Inconsistency Tolerance. Lecture Notes in Computer Science, vol. 3300. Springer, Heidelberg (2005). https://doi.org/10.1007/b104925

    Book  MATH  Google Scholar 

  3. Bueno-Soler, J., Carnielli, W.: Paraconsistent Probabilities: consistency, contradictions and Bayes’ theorem. Entropy 18(9), 325 (2016)

    Article  Google Scholar 

  4. Carnielli, W., Coniglio, M.E., Marcos, J.: Logics of formal inconsistency. In: Gabbay, D., Guenthner, F. (eds.) Handbook of Philosophical Logic. Handbook of Philosophical Logic, vol. 14, pp. 1–93. Springer, Dordrecht (2007). https://doi.org/10.1007/978-1-4020-6324-4_1

    Chapter  MATH  Google Scholar 

  5. Cignoli, R., D’Ottaviano, I.M.L., Mundici, D.: Algebraic Foundations of Many-valued Reasoning. Kluwer, Dordrecht (2000)

    Book  Google Scholar 

  6. Cintula, P., Hájek, P., Noguera, C., Fermüller, C. (eds.): Handbook of Mathematical Fuzzy Logic - volumes 1, 2 and 3. Studies in Logic, Mathematical Logic and Foundations, vol. 37, 38, 58. College Publications, London (2011, 2016)

    Google Scholar 

  7. Cintula, P., Noguera, C.: Modal logics of uncertainty with two-layer syntax: a general completeness theorem. In: Kohlenbach, U., Barceló, P., de Queiroz, R. (eds.) WoLLIC 2014. LNCS, vol. 8652, pp. 124–136. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44145-9_9

    Chapter  Google Scholar 

  8. De Bona, G., Finger, M.: Measuring inconsistency in probabilistic logic: rationality postulates and Dutch book interpretation. Artif. Intell. 227, 140–164 (2015)

    Article  MathSciNet  Google Scholar 

  9. De Bona, G., Finger, M., Potyka, N., Thimm, M.: Inconsistency measurement in probabilistic logic. In: Grant, J., Martinez, M.V. (eds.) Measuring Inconsistency in Information. Studies in Logic, vol. 73, pp. 235–269. College Publications (2018)

    Google Scholar 

  10. Flaminio, T., Godo, L.: A logic for reasoning about the probability of fuzzy events. Fuzzy Sets Syst. 158(6), 625–638 (2007)

    Article  MathSciNet  Google Scholar 

  11. Flaminio, T., Godo, L., Marchioni, E.: Reasoning about uncertainty of fuzzy events: an overview. In: Cintula, P. et al. (eds.) Understanding Vagueness - Logical, Philosophical, and Linguistic Perspectives, pp. 367–400. College Publications (2011)

    Google Scholar 

  12. Godo, L., Marchioni, E.: Coherent conditional probability in a fuzzy logic setting. Log. J. IGPL 14(3), 457–481 (2006)

    Article  MathSciNet  Google Scholar 

  13. Grant, J., Martinez, M.V. (eds.): Measuring Inconsistency in Information. Studies in Logic, vol. 73. College Publications (2018)

    Google Scholar 

  14. Hájek, P.: Metamathematics of Fuzzy Logic. Kluwer Academy Publishers (1998)

    Google Scholar 

  15. Hájek, P., Godo, L., Esteva, F.: Fuzzy logic and probability. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (UAI 1995), pp. 237–244 (1995)

    Google Scholar 

  16. Muiño, D.P.: Measuring and repairing inconsistency in probabilistic knowledge bases. Int. J. Approx. Reason. 52(6), 828–840 (2011)

    Article  MathSciNet  Google Scholar 

  17. Mundici, D.: Advanced Łukasiewicz Calculus and MV-Algebras. Springer, Dordrecht (2011). https://doi.org/10.1007/978-94-007-0840-2

    Book  MATH  Google Scholar 

  18. Potyka, N.: Linear programs for measuring inconsistency in probabilistic logics. In: Proceedings of KR 2014, pp. 568–577 (2014)

    Google Scholar 

  19. Potyka, N., Thimm, M.: Consolidation of probabilistic knowledge bases by inconsistency minimization. In: Proceedings of ECAI 2014, pp. 729–734 (2014)

    Google Scholar 

  20. Potyka, N., Thimm, M.: Probabilistic reasoning with inconsistent beliefs using inconsistency measures. In: Proceedings of IJCAI 2015, pp. 3156–3163 (2015)

    Google Scholar 

  21. Potyka, N., Thimm, M.: Inconsistency-tolerant reasoning over linear probabilistic knowledge bases. Int. J. Approx. Reason. 88, 209–236 (2017)

    Article  MathSciNet  Google Scholar 

  22. Thimm, M.: Measuring inconsistency in probabilistic knowledge bases. In: Proceedings of UAI 2009, pp. 530–537. AUAI Press (2009)

    Google Scholar 

  23. Thimm, M.: Inconsistency measures for probabilistic logics. Artif. Intell. 197, 1–24 (2013)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the anonymous reviewers for their helpful comments. The authors also acknowledge partial support by the MOSAIC project (EU H2020- MSCA-RISE-2020 Project 101007627). Flaminio and Godo also acknowledge support by the Spanish project ISINC (PID2019-111544GB-C21) funded by MCIN/AEI/10.13039/501100011033, while Ugolini also acknowledges the Marie Sklodowska-Curie grant agreement No. 890616 (H2020-MSCA-IF-2019).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lluis Godo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Flaminio, T., Godo, L., Ugolini, S. (2022). An Approach to Inconsistency-Tolerant Reasoning About Probability Based on Łukasiewicz Logic. In: Dupin de Saint-Cyr, F., Öztürk-Escoffier, M., Potyka, N. (eds) Scalable Uncertainty Management. SUM 2022. Lecture Notes in Computer Science(), vol 13562. Springer, Cham. https://doi.org/10.1007/978-3-031-18843-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-18843-5_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18842-8

  • Online ISBN: 978-3-031-18843-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics