Fusion, propagation, and structuring in belief networks
Artificial Intelligence
IEEE Transactions on Systems, Man and Cybernetics - Special issue on artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Epistemic entrenchment and possibilistic logic
Artificial Intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
A survey of belief revision and updating rules in various uncertainty models
Revision and updating in knowledge bases
Fuzzy relation equations and causal reasoning
Fuzzy Sets and Systems - Special issue: fuzzy relations, part 2
Learning Causes: Psychological Explanations of Causal Explanation^1
Minds and Machines
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Learning Bayesian networks with restricted causal interactions
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Models and selection criteria for regression and classification
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Editorial: fuzzy set and possibility theory-based methods in artificial intelligence
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Encoding fuzzy possibilistic diagnostics as a constrained optimization problem
Information Sciences: an International Journal
Development of possibilistic causal model from data
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
Hi-index | 0.00 |
The paper addresses uncertain reasoning based on causal knowledge given by two layered networks, where nodes in one layer express possible causes and those in the other are possible effects. Uncertainties of the causalities are given by conditional causal possibilities, which were proposed to express the exact degrees of possibility of causalities. The expression of the uncertainty also has an advantage over the conventional conditional possibilities in the number of possibilistic values that should be given as a priori knowledge. The number of conditional causal possibilities given as knowledge is far smaller than that of conventional conditional possibilities.The paper starts with the definition of a causal model called asymmetrically-valued causal model. The conditional causal possibilities are defined on the causal model, and their mathematical properties are discussed. Then, the paper defines the possibilistic causality consistency problem based on the proposed model and shows how to solve the problem. The discussed problem is one to calculate the conditional possibility of a hypothesis about presence and absence of unknown events when the states of some other events are known.