Titlebook: Mathematical Models for Handling Partial Knowledge in Artificial Intelligence; Giulianella Coletti,Didier Dubois,Romano Scozzafav Book 199

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Generalized Concept of Atoms for Conditional EventsThe problem of finding a “sound” logical framework upon conditional events is suggested by many authors with the aim of the “marriage” between the three-valued logic and the conditional probability logic.

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Checking the Coherence of Conditional Probabilities in Expert Systems: Remarks and AlgorithmsThe expert systems should help people make decisions with respect to complex problems. An important aspect connected to this concerns the need to offer the user a tool which assigns the probabilities of events in a coherent way.

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Algorithms for Precise and Imprecise Conditional Probability AssessmentsThe uncertainty treatment in Artificial Intelligence can be based on numerical and non numerical methods. Among the numerical methods different uncertainty measures have been proposed in literature to manage vague information and imprecise data.

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Mathematical Foundations of Evidence Theorytain. In this case arguments in favour of and against hypotheses can be derived. These arguments may be weighed by their likelihoods and thereby the credibility and plausibility of different possible hypotheses can be obtained. This is, in a nutshell, the idea to be explored and developed in this article.

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Computing Symbolic Support Functions by Classical Theorem-Proving Techniques, (Kohlas, Monney, 1993) an extension of Shafer’s mathematical theory of evidence. It is one possibility among others to deal with uncertain knowledge in AI systems. The symbolic representation of uncertain knowledge allows to apply classical inference techniques.

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Mathematical Models for Handling Partial Knowledge in Artificial Intelligence

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Giulianella Coletti,Didier Dubois,Romano Scozzafav

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