Skip to main content

Many-Valued Dynamic Logic for Qualitative Decision Theory

  • Conference paper
Book cover New Directions in Rough Sets, Data Mining, and Granular-Soft Computing (RSFDGrC 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1711))

Abstract

This paper presents an integration of the dynamic logic semantics and rational decision theory. Logics for reasoning about the expected utilities of actions are proposed. The well-formed formulas of the logics are viewed as the possible goals to be achieved by the decision maker and the truth values of the formulas are considered as the utilities of the goals. Thus the logics are many-valued dynamic logics. Based on different interpretations of acts in the logics, we can model different decision theory paradigms, such as possibilistic decision theory and case-based decision theory.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Dubois, D., Prade, H.: Possibility theory as a basis for qualitative decision theory. In: Proc. of the 14th International Joint Conference on Artificial Intelligence, pp. 1924–1930. Morgan Kaufmann Publishers, San Francisco (1995)

    Google Scholar 

  2. Dubois, D., Prade, H.: Constraint satisfaction and decision under uncertainty based on qualitative possibility theory. In: Proc. of the 6th IEEE International Conference on Fuzzy Systems, pp. 23–30 (1997)

    Google Scholar 

  3. Dubois, D., Prade, H.: Possibilistic logic in decision. In: Proc.of the ECAI 1998 Workshop: Decision Theory Meets Artificial Intelligence-Qualitative and Quantitative Approaches, pp. 11–21 (1998)

    Google Scholar 

  4. Esteva, F., Garcia, P., Godo, L., Rodriguez, R.: A modal account of similarity-based reasoning. International Journal of Approximate Reasoning, 235–260 (1997)

    Google Scholar 

  5. Fargier, H., Lang, J., Sabbadin, R.: Towards qualitative approaches to multistage decision making. In: Proc. of the 6th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 31–36 (1996)

    Google Scholar 

  6. De Giacomo, G., Lenzerini, M.: PDL-based framework for reasoning about actions. In: Gori, M., Soda, G. (eds.) AI*IA 1995. LNCS (LNAI), vol. 992, pp. 103–114. Springer, Heidelberg (1995)

    Google Scholar 

  7. Gilboa, I., Schmeidler, D.: Case-based decision: An extended abstract. In: Prade, H. (ed.) Proc. of the 13th European Conference on Artificial Intelligence, pp. 706–710. John Wiley & Sons Ltd., Chichester (1998)

    Google Scholar 

  8. Godo, L., Rodriguez, R.: A similarity-based fuzzy modal logic (1998) (preprint)

    Google Scholar 

  9. Hájek, P.: Metamathematics of Fuzzy Logic. Kluwer Academic Publisher, Dordrecht (1998)

    Google Scholar 

  10. Harel, D.: Dynamic logic. In: Gabbay, D.M., Guenthner, F. (eds.) Handbook of Philosophical Logic. Extensions of Classical Logic, vol. II, pp. 497–604. D. Reidel Publishing Company, Dordrechtz (1984)

    Google Scholar 

  11. Jaffray, J.Y., Wakke, P.: Decision making with belief functions: compatibility and incompatibility with the sure-thing principle. Journal of Risk and Uncertainty 8, 255–271 (1994)

    Google Scholar 

  12. Kozen, D.: A probabilistic PDL. In: Proc. of the 15th ACM Symposium on Theory of Computing, pp. 291–297 (1983)

    Google Scholar 

  13. Liau, C.-J.: A logic for reasoning about action, preference, and commitment. In: Proc. of the 13th European Conference on Artificial Intelligence, pp. 552–556. John Wiley & Sons Ltd., Chichester (1998)

    Google Scholar 

  14. Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1(1), 3–28 (1978)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liau, CJ. (1999). Many-Valued Dynamic Logic for Qualitative Decision Theory. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-48061-7_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66645-5

  • Online ISBN: 978-3-540-48061-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics