Uncertainty in Artificial Intelligence
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Positive and Negative Explanations of Uncertain Reasoning in the Framework of Possibility Theory
Henri Farrency, Henri Prade
Abstract:
This paper presents an approach for developing the explanation capabilities of rule-based expert systems managing imprecise and uncertain knowledge. The treatment of uncertainty takes place in the framework of possibility theory where the available information concerning the value of a logical or numerical variable is represented by a possibility distribution which restricts its more or less possible values. We first discuss different kinds of queries asking for explanations before focusing on the two following types : i) how, a particular possibility distribution is obtained (emphasizing the main reasons only) ; ii) why in a computed possibility distribution, a particular value has received a possibility degree which is so high, so low or so contrary to the expectation. The approach is based on the exploitation of equations in max-min algebra. This formalism includes the limit case of certain and precise information.
Keywords:
Pages: 95-101
PS Link:
PDF Link: /papers/89/p95-farrency.pdf
BibTex:
@INPROCEEDINGS{Farrency89,
AUTHOR = "Henri Farrency and Henri Prade",
TITLE = "Positive and Negative Explanations of Uncertain Reasoning in the Framework of Possibility Theory",
BOOKTITLE = "Proceedings of the Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1989",
PAGES = "95--101"
}


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