Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
“Conditional inter-causally independent” node distributions, a property of “noisy-or” models
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
On finding effective courses of action in dynamic uncertain situations
On finding effective courses of action in dynamic uncertain situations
Evolutionary Computation
Learning Bayesian Networks
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The paper presents two schemes to identify the best combination of events that maximize (minimize) the probability of a target node in a Bayesian network (BN). One scheme is based on evolutionary algorithms (EA) while the other is a heuristic approach, named Sets of Actions Finder (SAF). The heuristic approach is similar to the hill-climbing search technique and works in polynomial time. Both SAF and EA are applied over hundreds of Bayesian networks and the results are compared with the ones obtained through exhaustive searches. The results show that both SAF and EA perform quite similarly but the time taken by SAF is smaller compared to EA.