Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On Spohn's rule for revision of beliefs
International Journal of Approximate Reasoning
Anytime deduction for probabilistic logic
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
Information Sciences: an International Journal
Conditional Independence in A Coherent Finite Setting
Annals of Mathematics and Artificial Intelligence
Locally Strong Coherence in Inference Processes
Annals of Mathematics and Artificial Intelligence
Conditional Events with Vague Information in Expert Systems
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
International Journal of Approximate Reasoning
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Our starting point is the approach to probabilistic logic through coherence; but we give up de Finetti's idea of a conditional event E\H being a 3-valued entity, with the third value being just an undetermined common value for all ordered pairs (E,H). We let instead the "third" value of E\H suitably depend on the given pair. In this way we get, through a direct assignment of conditional probability, a general theory of probabilistic reasoning able to encompass other approaches to uncertain reasoning, such as fuzziness and default reasoning. We are also able to put forward a meaningful concept of conditional independence, which avoids many of the usual inconsistencies related to logical dependence. We give an example in which we put together different kinds of information and show how coherent conditional probability can act as a unifying tool.