Belief structures, possibility theory and decomposable confidence measures on finite sets
Computers and Artificial Intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
General theory of cumulative inference
Proceedings of the 2nd international workshop on Non-monotonic reasoning
The logical view of conditioning and its application to possibility and evidence theories
International Journal of Approximate Reasoning
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Theoretical foundations for non-monotonic reasoning in expert systems
Logics and models of concurrent systems
What does a conditional knowledge base entail?
Proceedings of the first international conference on Principles of knowledge representation and reasoning
On the consistency of defeasible databases
Artificial Intelligence
Epistemic entrenchment and possibilistic logic
Artificial Intelligence
What does a conditional knowledge base entail?
Artificial Intelligence
On the specificity of a possibility distribution
Fuzzy Sets and Systems
Nonmonotonic inference based on expectations
Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (vol. 3)
A logical notion of conditional independence: properties and applications
Artificial Intelligence - Special issue on relevance
Handling Hard Rules and Default Rules in Possibilistic Logic
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
System Z: A Natural Ordering of Defaults with Tractable Applications to Nonmonotonic Reasoning
Proceedings of the 3rd Conference on Theoretical Aspects of Reasoning about Knowledge
Qualitative relevance and independence: a roadmap
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Possibilistic logic bases and possibilistic graphs
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Coping with the limitations of rational inference in the framework of possibility theory
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Combining probabilistic logic programming with the power of maximum entropy
Artificial Intelligence - Special issue on nonmonotonic reasoning
A logic programming framework for possibilistic argumentation with vague knowledge
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
International Journal of Approximate Reasoning
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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In nonmonotonic reasoning, the preferential system P is known to provide reasonable but very cautious conclusions, and in particular, preferential inference is blocked by the presence of “irrelevant” properties. When using Lehmann's rational closure, the inference machinery, which is then more productive, may still remain too cautious. These two types of inference can be represented using a possibility theory-based semantics. To overcome the cautiousness of system P, we first progressively augment preferential inference with two extensions which are in between system P and rational closure. Then, in order to overcome some remaining limitations of rational closure the second half of this paper focuses more particularly on the use of (contextual) independence assumptions of the form: the fact that δ is true (or is false) does not affect the validity of the rule “normally if α then β”. The modelling of such independence assumptions is discussed in the possibilistic framework. Algorithms are provided to jointly handle default rules and independence relations, and to check their multual consistency.