Uncertainty in Artificial Intelligence
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Automated Reasoning Using Possibilistic Logic: Semantics, Belief Revision and Variable Certainty Weights
Didier Dubois, Jerome Lang, Henri Prade
Abstract:
In this paper an approach to automated deduction under uncertainty,based on possibilistic logic, is proposed ; for that purpose we deal with clauses weighted by a degree which is a lower bound of a necessity or a possibility measure, according to the nature of the uncertainty. Two resolution rules are used for coping with the different situations, and the refutation method can be generalized. Besides the lower bounds are allowed to be functions of variables involved in the clause, which gives hypothetical reasoning capabilities. The relation between our approach and the idea of minimizing abnormality is briefly discussed. In case where only lower bounds of necessity measures are involved, a semantics is proposed, in which the completeness of the extended resolution principle is proved. Moreover deduction from a partially inconsistent knowledge base can be managed in this approach and displays some form of non-monotonicity.
Keywords:
Pages: 81-87
PS Link:
PDF Link: /papers/89/p81-dubois.pdf
BibTex:
@INPROCEEDINGS{Dubois89,
AUTHOR = "Didier Dubois and Jerome Lang and Henri Prade",
TITLE = "Automated Reasoning Using Possibilistic Logic: Semantics, Belief Revision and Variable Certainty Weights",
BOOKTITLE = "Proceedings of the Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1989",
PAGES = "81--87"
}


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