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Defeasible Logic

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Web Knowledge Management and Decision Support (INAP 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2543))

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Abstract

We often reach conclusions partially on the basis that we do not have evidence that the conclusion is false. A newspaper story warning that the local water supply has been contaminated would prevent a person from drinking water from the tap in her home. This suggests that the absence of such evidence contributes to her usual belief that her water is safe. On the other hand, if a reasonable person received a letter telling her that she had won a million dollars, she would consciously consider whether there was any evidence that the letter was a hoax or somehow misleading before making plans to spend the money. All to often we arrive at conclusions which we later retract when contrary evidence becomes available. The contrary evidence defeats our earlier reasoning. Much of our reasoning is defeasible in this way. Since around 1980, considerable research in AI has focused on how to model reasoning of this sort. In this paper, I describe one theoretical approach to this problem, discuss implementation of this approach as an extension of Prolog, and describe some application of this work to normative reasoning, learning, planning, and other types of automated reasoning.

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Author information

Authors and Affiliations

  1. Department of Philosophy and Artificial Intelligence Center, The University of Georgia, 30605, GA, Athens, USA

    Donald Nute

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  1. Donald Nute
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Editor information

Editors and Affiliations

  1. IF Computer Japan, 5-28-2 Sendagi, Bunkyo-ku, 113-0022, Tokyo, Japan

    Oskar Bartenstein

  2. Fraunhofer FIRST, Kekulé 7, 12489, Berlin, Germany

    Ulrich Geske

  3. think-cell Software GmbH, Invalidenstraße 34, 10115, Berlin, Germany

    Markus Hannebauer

  4. Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan

    Osamu Yoshie

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© 2003 Springer-Verlag Berlin Heidelberg

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Nute, D. (2003). Defeasible Logic. In: Bartenstein, O., Geske, U., Hannebauer, M., Yoshie, O. (eds) Web Knowledge Management and Decision Support. INAP 2001. Lecture Notes in Computer Science(), vol 2543. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36524-9_13

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  • DOI: https://doi.org/10.1007/3-540-36524-9_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00680-0

  • Online ISBN: 978-3-540-36524-2

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