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Artificial Intelligence
What does a conditional knowledge base entail?
Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (vol. 3)
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Artificial Intelligence
JELIA '98 Proceedings of the European Workshop on Logics in Artificial Intelligence
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Bridging Logical, Comparative, and Graphical Possibilistic Representation Frameworks
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
System Z: a natural ordering of defaults with tractable applications to nonmonotonic reasoning
TARK '90 Proceedings of the 3rd conference on Theoretical aspects of reasoning about knowledge
Complexity and Cautiousness Results for Reasoning from Partially Preordered Belief Bases
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Inheritance comes of age: applying nonmonotonic techniques to problems in industry
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Inconsistency management and prioritized syntax-based entailment
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
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Possibilistic logic offers a unified framework for revising prioritized pieces of information and for reasoning with conditional knowledge bases. A conditional assertion of the form "generally, if α is true then β is true" is interpreted as a constraint expressing that the possibility degree of having α ∧ β being true is greater than the possibility degree of having α ∧¬β being true. Recently, an important extension of possibilistic logic has been proposed to deal with partially preordered bases in order to avoid comparing unrelated pieces of information. This paper establishes relationships between reasoning from partially pre-ordered bases and reasoning from conditional knowledge bases. It contains two important contributions. The first contribution consists in identifying conditions under which a partially ordered belief base can be encoded as a set of conditional assertions, and conversely. In particular, we provide the correspondences between the concept of compatible possibility distributions used for conditional assertions and the one of compatible prioritized bases used for partially pre-ordered bases. The second important contribution of this paper consists in providing the computational complexity of reasoning with partially pre-ordered bases using the well-known possibilistic and inclusion-based policies.