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Artificial Intelligence
Artificial Intelligence
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We propose a syntactic possibilistic belief-change operator, which operates on a belief base of necessity-valued formulas. Such a base may be regarded as a finite and compact encoding of a possibility distribution over a possibly infinite set of interpretations. The proposed operator is designed so that it behaves like a semantic possibilistic belief-change operator for BDI agents recently proposed in the literature. The equivalence of the semantic and syntactic operators is then proved. Experimental results are presented. The aim of these experiments is to demonstrate that the cost of belief revision expressed in terms of the number of entailment checks required as well as the size of the belief base do not explode as the number of new pieces of information formulas supplied increases.