Skip to main content
SpringerLink
Log in

How to reason with uncertain knowledge

  • 7. Hybrid Approaches To Uncertainty
  • Conference paper
  • First Online:
Uncertainty in Knowledge Bases (IPMU 1990)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 521))

  • 153 Accesses

Abstract

In this paper a reasoning process is viewed as a process of constructing a partial model of the world we are reasoning about. This partial model is a syntactic representation of an epistemic partial semantic model. In such a partial model different views on the world we are reasoning about can be represented. Multiple views can be the result of updating a partial model with information containing a disjunction or information described by sentences like: ‘most humans have brown eyes’. If a proposition does not have to hold in every view, we cannot be certain of it. To express this two certainty measures for conclusions based on a partial model will be defined, a probability and a likelihood measure. The former will be used for conclusions that express an expectation while the latter will be used for conclusions that express an explanation. To show the value of the approach various applications of these certainty measures will be described.

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. Bacchus, F. 1989, A modest but well founded inheritance reasoner, IJCAI-89, 1104–1109.

    Google Scholar 

  2. Buchanan, B. G., Shortliffe, E. H. 1984, Rule-based expert systems: the MYCIN experiments of the Stanford Heuristic Programming Project, Addison-Wesley Publishing Company.

    Google Scholar 

  3. Carnap, R. 1950, Logical foundations of probability, The University of Chicago Press, Chicago.

    Google Scholar 

  4. Chandrasekaran, B., Tanner, M. C. 1986, Uncertainty handling in expert systems, in: Kanal, L. N., Lemmer, J. F. (eds), Uncertainty in Artificial Intelligence, North-Holland, Amsterdam 35–46.

    Google Scholar 

  5. Charniak, E. 1983, The Bayesian basis of common sense medical diagnosis, AAAI-83 70–73.

    Google Scholar 

  6. Cheeseman, P. 1985, In defense of probability, IJCAI-85 1002–1009.

    Google Scholar 

  7. Clancey, W. C. 1984, Classification problem solving, AAAI-84 49–55.

    Google Scholar 

  8. Etherington, D. W., Borgida, A., Brachman, R. J., Kautz, H. 1989, Vivid knowledge and tractable reasoning: preliminary report, IJCAI-89, 1146–1152.

    Google Scholar 

  9. Fine, T. N. 1973, Theories of probability, Academic Press, New York.

    Google Scholar 

  10. Gärdenfors, P., Knowledge in Flux: Modeling the Dynamics of Epistemic States, Bradford Books, MIT Press, Cambridge MA (1988).

    Google Scholar 

  11. Johnson-Laird, P. N. 1983, Mental models, Toward a cognitive science of language inferences and consciousness, Cambridge University Press, Cambridge.

    Google Scholar 

  12. Kyburg, H. E. 1983, Objective probabilities, AAAI-83 902–904.

    Google Scholar 

  13. Los, J. 1963, Semantic representation of the probability of formulas in formal theories, Studia Logica 14 183–196.

    Article  Google Scholar 

  14. Nilsson, N. J. 1986, Probabilistic logic, Artificial Intelligence 28 71–87.

    Article  Google Scholar 

  15. Nutter, J. T. 1987, Uncertainty and probability, IJCAI-87 373–379.

    Google Scholar 

  16. Peng, Y., Reggia, J. A. 1986, Plausibility in diagnostic hypothesis: the nature of simplicity, AAAI-86 140–145.

    Google Scholar 

  17. Roos, N., Models for reasoning with incomplete and uncertain knowledge PhD thesis, Delft (1991).

    Google Scholar 

  18. Shafer, G. 1976, A mathematical theory of evidence, Princeton University Press, Princeton.

    Google Scholar 

  19. Shastri, L. 1989, Default reasoning in semantic networks: a formalization of recognition and inheritance, Artificial intelligence 39 283–355.

    Article  Google Scholar 

  20. Smets, P., Transferable belief model versus bayesian model, ECAI-88 (1988) 495–500.

    Google Scholar 

  21. Tversky, A., Kahneman, D. 1982, Judgement under uncertainty: heuristics and biases, In: Wendt, D., Vlek, C. (eds), Utility, probability and human decision making, D. Reidel Publishing Company, Dordrecht, Netherlands 141–162.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Delft University of Technology (TU-Delft), P.O. Box 356, 2600 AJ, Delft, the Netherlands

    Nico Roos

  2. National Aerospace Laboratory (NLR), Amsterdam, the Netherlands

    Nico Roos

Authors
  1. Nico Roos
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Bernadette Bouchon-Meunier Ronald R. Yager Lotfi A. Zadeh

Rights and permissions

Reprints and permissions

Copyright information

© 1991 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Roos, N. (1991). How to reason with uncertain knowledge. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Uncertainty in Knowledge Bases. IPMU 1990. Lecture Notes in Computer Science, vol 521. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028127

Download citation

  • DOI: https://doi.org/10.1007/BFb0028127

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54346-6

  • Online ISBN: 978-3-540-47580-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation