Skip to main content

On the Dynamics of Total Preorders: Revising Abstract Interval Orders

  • Conference paper
Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4724))

Abstract

Total preorders (tpos) are often used in belief revision to encode an agent’s strategy for revising its belief set in response to new information. Thus the problem of tpo-revision is of critical importance to the problem of iterated belief revision. Booth et al. [1] provide a useful framework for revising tpos by adding extra structure to guide the revision of the initial tpo, but this results in single-step tpo revision only. In this paper we extend that framework to consider double-step tpo revision. We provide new ways of representing the structure required to revise a tpo, based on abstract interval orders, and look at some desirable properties for revising this structure. We prove the consistency of these properties by giving a concrete operator satisfying all of them.

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. Booth, R., Meyer, T., Wong, K.S.: A bad day surfing is better than a good day working:How to revise a total preorder. In: Proceedings of KR 2006, pp. 230–238 (2006)

    Google Scholar 

  2. Hansson, S.O.: A Textbook of Belief Dynamics. Kluwer Academic Publishers, Dordrecht (1999)

    Book  MATH  Google Scholar 

  3. Grove, A.: Two modellings for theory change. Journal of Philosophical Logic 17, 157–170 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  4. Katsuno, H., Mendelzon, A.: Propositional knowledge base revision and minimal change. Artificial Intelligence 52, 263–294 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  5. Booth, R., Meyer, T.: Admissible and restrained revision. Journal of Artificial Intelligence Research 26, 127–151 (2006)

    MathSciNet  MATH  Google Scholar 

  6. Darwiche, A., Pearl, J.: On the logic of iterated belief revision. Artificial Intelligenc 89, 1–29 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  7. Nayak, A., Pagnucco, M., Peppas, P.: Dynamic belief change operators. Artificial Intelligence 146, 193–228 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  8. Spohn, W.: Ordinal conditional functions: A dynamic theory of epistemic states. In: Causation in Decision: Belief, Change and Statistics, pp. 105–134. Kluwer Academic Publishers, Dordrecht (1988)

    Chapter  Google Scholar 

  9. Papini, O.: Iterated revision operations stemming from the history of an agent’s observations. In: Frontiers of belief revision, pp. 281–303. Kluwer AcademicPublishers, Dordrecht (2001)

    Google Scholar 

  10. Hansson, S.O.: A survey of non-prioritized belief revision. Erkenntnis 50, 413–427 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  11. Jin, Y., Thielscher, M.: Iterated belief revision, revised. In: Proceedings of IJCAI 2005, pp. 478–483 (2005)

    Google Scholar 

  12. Öztürk, M., Tsoukiás, A., Vincke, P.: Preference modelling. In: Multiple CriteriaDecision Analysis: State of the Art Surveys, pp. 27–72. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  13. Allen, J.: Maintaining knowledge about temporal intervals. Journal of the ACM 26, 832–843 (1983)

    Article  MATH  Google Scholar 

  14. Goldszmidt, M., Pearl, J.: Qualitative probabilities for default reasoning, beliefrevision and causal modeling. Artificial Intelligence 84, 57–112 (1996)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Booth, R., Meyer, T. (2007). On the Dynamics of Total Preorders: Revising Abstract Interval Orders. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-75256-1_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75255-4

  • Online ISBN: 978-3-540-75256-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics