Algorithms for precise and imprecise conditional probability assessments
Mathematical models for handling partial knowledge in artificial intelligence
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Probabilistic Reasoning Under Coherence in System P
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
Probabilistic Consistency of Conditional Probability Bounds
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
Models and algorithms for probabilistic and Bayesian logic
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Locally Strong Coherence in Inference Processes
Annals of Mathematics and Artificial Intelligence
Probabilistic Reasoning Under Coherence in System P
Annals of Mathematics and Artificial Intelligence
On the checking of G-coherence of conditional probability bounds
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Probabilistic logic under coherence: complexity and algorithms
Annals of Mathematics and Artificial Intelligence
Probabilistic abduction without priors
International Journal of Approximate Reasoning
Some theoretical properties of conditional probability assessments
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Generalizing inference rules in a coherence-based probabilistic default reasoning
International Journal of Approximate Reasoning
Conditional random quantities and iterated conditioning in the setting of coherence
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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We use imprecise probabilities, based on a concept of generalized coherence, for the management of uncertainty in artificial intelligence. With the aim of reducing the computational difficulties, in the checking of generalized coherence we propose a method which exploits, in the framework of the betting criterion, suitable subsets of the sets of values of the random gains. We give an algorithm in each step of which a linear system with a reduced number of unknowns can be used. Our method improves a procedure already existing in literature and could be integrated with recent approaches of other authors, who exploit suitable logical conditions with the aim of splitting the problem into subproblems. We remark that our approach could be also used in combination with efficient methods like column generation techniques. Finally, to illustrate our method, we give some examples.