A theory of diagnosis from first principles
Artificial Intelligence
Discrete mathematics and its applications (2nd ed.)
Discrete mathematics and its applications (2nd ed.)
Beliefs, belief revision, and splitting languages
Logic, language and computation, vol. 2
Relevance sensitive belief structures
Annals of Mathematics and Artificial Intelligence
Decision Support Systems and Intelligent Systems (7th Edition)
Decision Support Systems and Intelligent Systems (7th Edition)
A Framework of Fuzzy Diagnosis
IEEE Transactions on Knowledge and Data Engineering
Negotiation as mutual belief revision
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Reasoning about bargaining situations
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Propositional independence: formula-variable independence and forgetting
Journal of Artificial Intelligence Research
An ordinal bargaining solution with fixed-point property
Journal of Artificial Intelligence Research
Representation theorems for multiple belief changes
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Hi-index | 0.00 |
Belief revision, as one central problem in artificial intelligence, is of interest in decision-making and game theory. To deal with local belief change that is a desirable property for belief revision, a novel notion - the finest splitting was proposed by Parikh, Kourousias and Makinson, respectively. But it was not clear how to construct the finest splitting of a propositional theory (a closed set of propositional formulae). In this paper, we propose a constructive method, that is intractable generally, to compute the finest splitting of a propositional theory. We also propose a polynomial time algorithm to compute the finest splitting of a propositional theory consisting of clauses. As an application in the diagnosis theory, we show that, given a diagnosis system (SD,COMP,OBS), it is quite easy to compute all of its diagnosis if the splitting of SD@?OBS is pre-computed. Additionally, in terms of this approach, we can have more specific reason to interpret observation than the original one.