Skip to main content
SpringerLink
Log in

Handling incomplete knowledge in artificial intelligence

  • Conference paper
  • First Online:
Information Systems and Artificial Intelligence: Integration Aspects (IS/KI 1990)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 474))

Included in the following conference series:

Abstract

In this paper we first discuss the important role of nonmonotonic reasoning for Artificial Intelligence. After presenting some simple forms of nonmonotonicity as they arise in various well-known AI systems we present in Section 2 some of the most important existing nonmonotonic logics: McCarthy's circumscription, Moore's autoepistemic logic, and Reiter's default logic. Section 3 examines an approach in which default reasoning is reduced to reasoning in the presence of inconsistent information. The approach is based on the notion of preferred maximal consistent subsets. It is shown that these preferred subsets can be defined in such a way that it is possibly to represent priorities between defaults adequately. Section 4 briefly discusses the problem of implementing nonmonotonic systems.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brewka, Gerhard: The Logic of Inheritance in Frame Systems, Proc. IJCAI 87, 1987

    Google Scholar 

  2. Brewka, Gerhard: Nonmonotonic Reasoning — Logical Foundations of Commonsense, Cambridge Tracts in Theoretical Computer Science 12, Cambridge University Press, 1990

    Google Scholar 

  3. Brewka, Gerhard: Cumulative Default Logic — In Defense of Nonmonotonic Inference Rules, Artificial Intelligence, to appear

    Google Scholar 

  4. Doyle, J.: A Truth Maintenance System, Artificial Intelligence 12, 1979

    Google Scholar 

  5. Freitag, H., Reinfrank, M.: A Non-Monotonic Deduction System Based on (A)TMS, Proc. ECAI 88, München, 1988

    Google Scholar 

  6. Gärdenfors, Peter: Knowledge in Flux, MIT Press, Cambridge, MA, 1988

    Google Scholar 

  7. Gärdenfors, Peter, Makinson, David: Revisions of Knowledge Systems Using Epistemic Entrenchment. In: Vardi, M. (ed): Proceedings of the Second Conference on Theoretical Aspects of Reasoning about Knowledge, Morgan Kaufmann, Los Altos, 1988

    Google Scholar 

  8. Gelfond, Michael, Lifschitz, Vladimir: Compiling Circumscriptive Theories into Logic Programs, Proc. 2nd Int. Workshop on Nonmonotonic Reasoning, Springer, LNCS 346, 1988

    Google Scholar 

  9. Goodwin, James W.: A Theory and System for Non-Monotonic Reasoning, Linköping University, Computer and Information Science Dep., Dissertation No. 165, 1987

    Google Scholar 

  10. Gordon, Thomas F.: Oblog 2 — A Hybrid Knowledge Representation System for Defeasible Reasoning, Proc. 1st Intl. Conference on Artificial Intelligence and Law, Boston, ACM Press, 1987

    Google Scholar 

  11. Hanks, Steven, McDermott, Drew: Nonmonotonic Logic and Temporal Projection, Artificial Intelligence 33, 1987

    Google Scholar 

  12. Junker, Ulrich, Konolige, Kurt: Computing the Extensions of Autoepistemic and Default Logic with a TMS, Proc. AAAI 90, 1990

    Google Scholar 

  13. Konolige, Kurt: On the Relation Between Default and Autoepistemic Logic, Artificial Intelligence 35 (3), 1988

    Google Scholar 

  14. Lifschitz, Vladimir: Computing Circumscription, IJCAI 85, 1985

    Google Scholar 

  15. McCarthy, John: Circumscription — A Form of Nonmonotonic Reasoning, Artificial Intelligence 13, 1980

    Google Scholar 

  16. McCarthy, John: Applications of Circumscription to Formalizing Common Sense Knowledge, Proc. AAAI-Workshop Non-Monotonic Reasoning, 1984 (auch in Artificial Intelligence 28, 1986)

    Google Scholar 

  17. Moore, Robert C.: Semantical Considerations on Nonmonotonic Logic, Artificial Intelligence 25, 1985 (Kurzfassung in Proc. IJCAI 83)

    Google Scholar 

  18. Petrie, C.J.: Revised Dependency-Directed Backtracking for Default Reasoning, Proc. AAAI 87, 1987

    Google Scholar 

  19. Poole, D.: A Logical Framework for Default Reasoning, Artificial Intelligence 36, 1988

    Google Scholar 

  20. Poole, D., Goebel, R., Aleliunas, R.: A Logical Reasoning System for Defaults and Diagnosis, University of Waterloo, Dep. of Computer Science, Research Rep. CS-86-06, 1986

    Google Scholar 

  21. Reiter, Raymond: A Logic for Default Reasoning, Artificial Intelligence 13, 1980

    Google Scholar 

  22. Rescher, Nicholas: Hypothetical Reasoning, North-Holland Publ., Amsterdam. 1964

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. GMD, Postfach 12 40, 5205, Sankt Augustin, W. Germany

    Gerhard Brewka

Authors
  1. Gerhard Brewka
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Dimitris Karagiannis

Rights and permissions

Reprints and permissions

Copyright information

© 1991 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Brewka, G. (1991). Handling incomplete knowledge in artificial intelligence. In: Karagiannis, D. (eds) Information Systems and Artificial Intelligence: Integration Aspects. IS/KI 1990. Lecture Notes in Computer Science, vol 474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-53557-8_19

Download citation

  • DOI: https://doi.org/10.1007/3-540-53557-8_19

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-53557-7

  • Online ISBN: 978-3-540-46809-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation