Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Reasoning under incomplete information in artificial intelligence: a comparison of formalisms using a single example
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Evidence, knowledge, and belief functions
International Journal of Approximate Reasoning - Special issue: The belief functions revisited: questions and answers
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Computing probability intervals under independency constraints
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Generalizing inference rules in a coherence-based probabilistic default reasoning
International Journal of Approximate Reasoning
Quasi conjunction, quasi disjunction, t-norms and t-conorms: Probabilistic aspects
Information Sciences: an International Journal
| Hi-index | 0.00 |
An approach to reasoning with default rules where the proportion of exceptions, or more generally the probability of encountering an exception, can be at least roughly assessed is presented. It is based on local uncertainty propagation rules which provide the best bracketing of a conditional probability of interest from the knowledge of the bracketing of some other conditional probabilities. A procedure that uses two such propagation rules repeatedly is proposed in order to estimate any simple conditional probability of interest from the available knowledge. The iterative procedure, that does not require independence assumptions, looks promising with respect to the linear programming method. Improved bounds for conditional probabilities are given when independence assumptions hold.