Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Learning from imprecise data: possibilistic graphical models
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Three Scenarios for the Revision of Epistemic States*
Journal of Logic and Computation
Possibilistic causal networks for handling interventions: a new propagation algorithm
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Revision sequences and nested conditionals
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
A Framework for Iterated Belief Revision Using Possibilistic Counterparts to Jeffrey's Rule
Fundamenta Informaticae - Methodologies for Intelligent Systems
Independence in possibility theory under different triangular norms
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Syntactic computation of hybrid possibilistic conditioning under uncertain inputs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Conditioning, belief update and revision are important tasks for designing intelligent systems. Possibility theory is among the powerful uncertainty theories particularly suitable for representing and reasoning with uncertain and incomplete information. This paper addresses an important issue related to the possibilistic counterparts of Jeffrey's rule of conditioning. More precisely, it addresses the existence and uniqueness of the solutions computed using the possibilistic counterparts of the so-called kinematics properties underlying Jeffrey's rule of conditioning. We first point out that like the probabilistic framework, in the quantitative possibilistic setting, there exists a unique solution for revising a possibility distribution given the uncertainty bearing on a set of exhaustive and mutually exclusive events. However, in the qualitative possibilistic framework, the situation is different. In particular, the application of Jeffrey's rule of conditioning does not guarantee the existence of a solution. We provide precise conditions where the uniqueness of the revised possibility distribution exists.