Skip to main content
SpringerLink
Log in

A Query-Driven Anytime Algorithm for Argumentative and Abductive Reasoning

  • Conference paper
  • First Online:
Soft-Ware 2002: Computing in an Imperfect World (Soft-Ware 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2311))

Abstract

This paper presents a new approximation methodfor computing arguments or explanations in the context of logic-basedargumentative or abductive reasoning. The algorithm can be interrupted at any time returning the solution foundso far. The quality of the approximation increases monotonically when more computational resources are available. The methodis basedon cost functions andreturns lower and upper bounds. 1

Research supportedb y scholarship No. 8220-061232 of the Swiss National Science Foundation.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. B. Anrig, R. Bissig, R. Haenni, J. Kohlas, and N. Lehmann. Probabilistic argumentation systems: Introduction to assumption-based modeling with ABEL. Technical Report 99-1, Institute of Informatics, University of Fribourg, 1999.

    Google Scholar 

  2. K. B. Laskey and P. E. Lehner. Assumptions, beliefs andprobabilities. Artificial Intelligence, 41(1):65–77, 1989.

    Article  MathSciNet  Google Scholar 

  3. D. Berzati, R. Haenni, and J. Kohlas. Probabilistic argumentation systems and abduction. In C. Baral and M. Truszczynski, editors, Proccedings of the 8th International Workshop on Non-Monotonic Reasoning, Breckenridge Colorado, 2000.

    Google Scholar 

  4. J. Bigham, Z. Luo, and D. Banerjee. A cost boundedp ossibilistic ATMS. In Christine Froidevaux and Jürg Kohlas, editors, Proceedings of the ECSQARU Conference on Symbolic and Quantitive Approaches to Reasoning and Uncertainty, volume 946, pages 52–59, Berlin, 1995. Springer Verlag.

    Google Scholar 

  5. J. W. Collins and D. de Coste. CATMS: An ATMS which avoids label explosions. In Kathleen McKeown and Thomas Dean, editors, Proceedings of the 9th National Conference on Artificial Intelligence, pages 281–287. MIT Press, 1991.

    Google Scholar 

  6. J. de Kleer. An assumption-based TMS. Artificial Intelligence, 28:127–162, 1986.

    Article  Google Scholar 

  7. J. de Kleer. Extending the ATMS. Artificial Intelligence, 28:163–196, 1986.

    Article  Google Scholar 

  8. J. de Kleer. Problem solving with the ATMS. Artificial Intelligence, 28:197–224, 1986.

    Article  Google Scholar 

  9. J. de Kleer. Focusing on probable diagnoses. In Kathleen Dean, Thomas L.; McKeown, editor, Proceedings of the 9th National Conference on Artificial Intelligence, pages 842–848. MIT Press, 1991.

    Google Scholar 

  10. J. de Kleer. A perspective on assumption-based truth maintenance. Artificial Intelligence, 59(1–2):63–67, 1993.

    Article  Google Scholar 

  11. T. Dean and M. Boddy. An analysis of time-dependent planning. In Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), pages 49–54. MIT Press, 1988.

    Google Scholar 

  12. T. L. Dean. Intrancibility and time-dependent planning. In M. P. Geofrey and A.L. Lansky, editors, Proceedings of the 1986 Workshop on Reasoning about Actions and Plans. Morgan Kaufmann Publishers, 1987.

    Google Scholar 

  13. R. Dechter. Bucket elimination: A unifying framework for reasoning. Artificial Intelligence, 113(1–2):41–85, 1999.

    Article  MATH  MathSciNet  Google Scholar 

  14. K. D. Forbus and J. de Kleer. Focusing the ATMS. In Tom M. Smith and Reid G. Mitchell, editors, Proceedings of the 7th National Conference on Artificial Intelligence, pages 193–198, St. Paul, MN, 1988. Morgan Kaufmann.

    Google Scholar 

  15. R. Haenni. Modeling uncertainty with propositional assumption-based systems. In S. Parson and A. Hunter, editors, Applications of Uncertainty Formalisms, Lecture Notes in Artifical Intelligence 1455, pages 446–470. Springer-Verlag, 1998.

    Google Scholar 

  16. R. Haenni. Cost-bounded argumentation. International Journal of Approximate Reasoning, 26(2):101–127, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  17. R. Haenni, J. Kohlas, and N. Lehmann. Probabilistic argumentation systems. In J. Kohlas and S. Moral, editors, Handbook of Defeasible Reasoning and Uncertainty Management Systems, Volume 5: Algorithms for Uncertainty and Defeasible Reasoning. Kluwer, Dordrecht, 2000.

    Google Scholar 

  18. K. Inoue. An abductive procedure for the CMS/ATMS. In J. P. Martins and M. Reinfrank, editors, Truth Maintenance Systems, Lecture Notes in A.I., pages 34–53. Springer, 1991.

    Google Scholar 

  19. J. Kohlas and P. A. Monney. A Mathematical Theory of Hints. An Approach to the Dempster-Shafer Theory of Evidence, volume 425 of Lecture Notes in Economics and Mathematical Systems. Springer, 1995.

    Google Scholar 

  20. M. McLeish. Nilsson’s probabilistic entailment extended to Dempster-Shafer theory. In L. N. Kanal, T. S. Levitt, and J. F. Lemmer, editors, Uncertainty in Artificial Intelligence, volume 8, pages 23–34. North-Holland, Amsterdam, 1987.

    Google Scholar 

  21. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1988.

    Google Scholar 

  22. G. M. Provan. A logic-basedanalysis of Dempster-Shafer theory. International Journal of Approximate Reasoning, 4:451–495, 1990.

    Article  MathSciNet  MATH  Google Scholar 

  23. R. Reiter and J. de Kleer. Foundations of assumption-based truth maintenance systems: Preliminary report. In Kenneth Forbus and Howard Shrobe, editors, Proceedings of the Sixth National Conference on Artificial Intelligence, pages 183–188. American Association for Artificial Intelligence, AAAI Press, 1987.

    Google Scholar 

  24. G. Shafer. The Mathematical Theory of Evidence. Princeton University Press, 1976.

    Google Scholar 

  25. P. P. Shenoy. Binary join trees. In Eric Horvitz and Finn Jensen, editors, Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence (UAI-96), pages 492–499, San Francisco, 1996. Morgan Kaufmann Publishers.

    Google Scholar 

  26. Ph. Smets. Probability of provability and belief functions. In M. Clarke, R. Kruse R., and S. Moral, editors, Proceedings of the ECSQARU’93 conference, pages 332–340. Springer-Verlag, 1993.

    Google Scholar 

  27. Ph. Smets and R. Kennes. The transferable belief model. Artificial Intelligence, 66:191–234, 1994.

    Article  MATH  MathSciNet  Google Scholar 

  28. S. Zilberstein. Using anytime algorithms in intelligent systems. AI Magazine, Fall:71–83, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of California, 90095, Los Angeles, CA

    Rolf Haenni

Authors
  1. Rolf Haenni
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Informatics School of Information and Software Engineering, University of Ulster, Jordanstown Campus, BT37 0QB, Newtownabbey, Northern Ireland

    David Bustard , Weiru Liu  & Roy Sterritt ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Haenni, R. (2002). A Query-Driven Anytime Algorithm for Argumentative and Abductive Reasoning. In: Bustard, D., Liu, W., Sterritt, R. (eds) Soft-Ware 2002: Computing in an Imperfect World. Soft-Ware 2002. Lecture Notes in Computer Science, vol 2311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46019-5_9

Download citation

  • DOI: https://doi.org/10.1007/3-540-46019-5_9

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43481-8

  • Online ISBN: 978-3-540-46019-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation