Skip to main content

Similarity, Approximations and Vagueness

  • Conference paper
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005)

Abstract

The relation of similarity is essential in understanding and developing frameworks for reasoning with vague and approximate concepts. There is a wide spectrum of choice as to what properties we associate with similarity and such choices determine the nature of vague and approximate concepts defined in terms of these relations. Additionally, robotic systems naturally have to deal with vague and approximate concepts due to the limitations in reasoning and sensor capabilities. Halpern [1] introduces the use of subjective and objective states in a modal logic formalizing vagueness and distinctions in transitivity when an agent reasons in the context of sensory and other limitations. He also relates these ideas to a solution to the Sorities and other paradoxes. In this paper, we generalize and apply the idea of similarity and tolerance spaces [2,3,4,5], a means of constructing approximate and vague concepts from such spaces and an explicit way to distinguish between an agent’s objective and subjective states. We also show how some of the intuitions from Halpern can be used with similarity spaces to formalize the above-mentioned Sorities and other paradoxes.

Supported in part by a WITAS UAV project grant under the Wallenberg Foundation, Sweden and an NFFP03 grant (COMPAS).

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. Halpern, J.: Intransitivity and vagueness. In: Dubois, D., Welty, C., Williams, M.A. (eds.) Proc. of 9th Int. Conf. KR 2004, pp. 121–129. AAAI Press, Menlo Park (2004)

    Google Scholar 

  2. Doherty, P., Szałas, A.: On the correspondence between approximations and similarity. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 143–152. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  3. Doherty, P., Łukaszewicz, W., Szałas, A.: Tolerance spaces and approximative representational structures. In: Günter, A., Kruse, R., Neumann, B. (eds.) KI 2003. LNCS (LNAI), vol. 2821, pp. 475–489. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  4. Doherty, P., Łukaszewicz, W., Szałas, A.: Approximate databases and query techniques for agents with heterogenous perceptual capabilities. In: Proc. of the 7th Int. Conf. on Information Fusion, FUSION 2004, pp. 175–182 (2004)

    Google Scholar 

  5. Doherty, P., Łukaszewicz, W., Szałas, A.: Approximative query techniques for agents with heterogeneous ontologies and perceptive capabilities. In: Dubois, D., Welty, C., Williams, M.A. (eds.) Proc. of the 9th Int. Conf. KR 2004, pp. 459–468. AAAI Press, Menlo Park (2004)

    Google Scholar 

  6. Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  7. Klir, G., Folger, T.: Fuzzy Sets, Uncertainty, and Information. Prentice-Hall, Englewood Cliffs (1988)

    MATH  Google Scholar 

  8. Williamson, T.: Vagueness. Routledge (1994)

    Google Scholar 

  9. Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27, 245–253 (1996)

    MATH  MathSciNet  Google Scholar 

  10. Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley, Reading (1996)

    Google Scholar 

  11. Zadeh, L.: Fuzzy sets. Information and Control 8, 333–353 (1965)

    Article  MathSciNet  Google Scholar 

  12. Zadeh, L.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22, 73–84 (2001)

    Google Scholar 

  13. Murai, T., Kanemitsu, H., Shimbo, M.: Fuzzy sets and binary-proximity-based rough sets. Information Sciences 104, 49–80 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  14. Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems 17, 191–209 (1990)

    Article  MATH  Google Scholar 

  15. Dubois, D., Prade, H.: Putting rough sets and fuzzy sets together. In: Slowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, pp. 204–232. Kluwer, Dordrecht (1992)

    Google Scholar 

  16. McCarthy, J.: Approximate objects and approximate theories. In: Cohn, A., Giunchiglia, F., Selman, B. (eds.) Proc. 7th Int. Conf. KR 2000, San Francisco, Ca., pp. 519–526. Morgan Kaufmann Pub., Inc, San Francisco (2000)

    Google Scholar 

  17. Kautz, H., Selman, B.: Knowledge compilation and theory approximation. Journal of the ACM 43, 193–224 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  18. Lin, F.: On strongest necessary and weakest sufficient conditions. In: Cohn, A., Giunchiglia, F., Selman, B. (eds.) Proc. 7th Int. Conf. KR’2000, pp. 167–175. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  19. Cadoli, M.: Tractable Reasoning in Aritificial Intelligence. LNCS, vol. 941. Springer, Heidelberg (1995)

    Google Scholar 

  20. Doherty, P., Łukaszewicz, W., Szałas, A.: Computing strongest necessary and weakest sufficient conditions of first-order formulas. In: Proc. IJCAI 2001, pp. 145–151 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Doherty, P., Łukaszewicz, W., Szałas, A. (2005). Similarity, Approximations and Vagueness. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_56

Download citation

  • DOI: https://doi.org/10.1007/11548669_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28653-0

  • Online ISBN: 978-3-540-31825-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics