Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Automatic structuring and retrieval of large text files
Communications of the ACM
Decision-theoretic troubleshooting
Communications of the ACM
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Probabilistic fault localization in communication systems using belief networks
IEEE/ACM Transactions on Networking (TON)
Learning Bayesian Networks
MUNIN: a causal probabilistic network for interpretation of electromyographic findings
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
How to elicit many probabilities
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Bayesian Networks (BNs) have been extensively used for diagnosis applications. Knowledge acquisition (KA), i.e. building a BN from the knowledge of experts in the application domain, involves two phases: knowledge gathering and model construction, i.e. defining the model based on that knowledge. The number of parameters involved in a large network is normally intractable to be specified by human experts. This leads to a trade-off between the accuracy of a detailed model and the size and complexity of such a model. In this paper, a Knowledge Acquisition Tool (KAT) to automatically perform information gathering and model construction for diagnosis of the radio access part of cellular networks is presented. KAT automatically builds a diagnosis model based on the experts’ answers to a sequence of questions regarding his way of reasoning in diagnosis. This will be performed for two BN structures: Simple Bayes Model (SBM) and Independence of Causal Influence (ICI) models.