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1987 Volume 2
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RESEARCH ARTICLE   Open Access    

An AI view of the treatment of uncertainty

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  • Abstract: This paper reviews many of the very varied concepts of uncertainty used in AI. Because of their great popularity and generality “parallel certainty inference” techniques, so-called, are prominently in the foreground. We illustrate and comment in detail on three of these techniques; Bayes' theory (section 2); Dempster-Shafer theory (section 3); Cohen's model of endorsements (section 4), and give an account of the debate that has arisen around each of them. Techniques of a different kind (such as Zadeh's fuzzy-sets, fuzzy-logic theory, and the use of non-standard logics and methods that manage uncertainty without explicitly dealing with it) may be seen in the background (section 5).The discussion of technicalities is accompanied by a historical and philosophical excursion on the nature and the use of uncertainty (section 1), and by a brief discussion of the problem of choosing an adequate AI approach to the treatment of uncertainty (section 6). The aim of the paper is to highlight the complex nature of uncertainty and to argue for an open-minded attitude towards its representation and use. In this spirit the pros and cons of uncertainty treatment techniques are presented in order to reflect the various uncertainty types. A guide to the literature in the field, and an extensive bibliography are appended.
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  • Cite this article

    Alessandro Saffiotti. 1987. An AI view of the treatment of uncertainty. The Knowledge Engineering Review. 2:5 doi: 10.1017/S0269888900000795
    Alessandro Saffiotti. 1987. An AI view of the treatment of uncertainty. The Knowledge Engineering Review. 2:5 doi: 10.1017/S0269888900000795
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RESEARCH ARTICLE   Open Access    

An AI view of the treatment of uncertainty

  • Published online: 07 July 2009
The Knowledge Engineering Review  2,  Article number: 10.1017/S0269888900000795  (1987)  |  Cite this article

Abstract: Abstract: This paper reviews many of the very varied concepts of uncertainty used in AI. Because of their great popularity and generality “parallel certainty inference” techniques, so-called, are prominently in the foreground. We illustrate and comment in detail on three of these techniques; Bayes' theory (section 2); Dempster-Shafer theory (section 3); Cohen's model of endorsements (section 4), and give an account of the debate that has arisen around each of them. Techniques of a different kind (such as Zadeh's fuzzy-sets, fuzzy-logic theory, and the use of non-standard logics and methods that manage uncertainty without explicitly dealing with it) may be seen in the background (section 5).The discussion of technicalities is accompanied by a historical and philosophical excursion on the nature and the use of uncertainty (section 1), and by a brief discussion of the problem of choosing an adequate AI approach to the treatment of uncertainty (section 6). The aim of the paper is to highlight the complex nature of uncertainty and to argue for an open-minded attitude towards its representation and use. In this spirit the pros and cons of uncertainty treatment techniques are presented in order to reflect the various uncertainty types. A guide to the literature in the field, and an extensive bibliography are appended.

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    Alessandro Saffiotti. 1987. An AI view of the treatment of uncertainty. The Knowledge Engineering Review. 2:5 doi: 10.1017/S0269888900000795
    Alessandro Saffiotti. 1987. An AI view of the treatment of uncertainty. The Knowledge Engineering Review. 2:5 doi: 10.1017/S0269888900000795
    shu

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