Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Valuation-based systems: a framework for managing uncertainty in expert systems
Fuzzy logic for the management of uncertainty
International Journal of Approximate Reasoning
Computing marginals for arbitrary subsets from marginal representation in Markov trees
Artificial Intelligence
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Generation of Classification Rules
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
An Introduction to Algorithms for Inference in Belief Nets
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
A new algorithm for finding MAP assignments to belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
| Hi-index | 0.00 |
In the literature, the optimization problem to identify a set of composite hypotheses H, which will yield the k largest P(H|S e) where a composite hypothesis is an instantiation of all the nodes in the network except the evidence nodes [17] is of significant interest. This problem is called “finding the k Most Plausible Explanation (MPE) of a given evidence S e in a Bayesian belief network”. The problem of finding k most probable hypotheses is generally NP-hard [2]. Therefore in the past various simplifications of the task by restricting k (to 1 or 2), restricting the structure (e.g. to singly connected networks), or shifting the complexity to spatial domain have been investigated. A genetic algorithm is proposed in this paper to overcome some of these restrictions while stepping out from probabilistic domain onto the general Valuation based System (VBS) framework is also proposed by generalizing the genetic algorithm approach to the realm of Dempster-Shafer belief calculus.