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Handling incomplete knowledge in artificial intelligence

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Information Systems and Artificial Intelligence: Integration Aspects (IS/KI 1990)

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

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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.

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Authors and Affiliations

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

    Gerhard Brewka

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  1. Gerhard Brewka
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Dimitris Karagiannis

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© 1991 Springer-Verlag Berlin Heidelberg

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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

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

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-53557-7

  • Online ISBN: 978-3-540-46809-7

  • eBook Packages: Springer Book Archive

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