Use of Elvira's explanation facility for debugging probabilistic expert systems
Knowledge-Based Systems
Generating Explanations Based on Markov Decision Processes
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Variable elimination for influence diagrams with super value nodes
International Journal of Approximate Reasoning
Dealing with complex queries in decision-support systems
Data & Knowledge Engineering
A multivariate probabilistic method for comparing two clinical datasets
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Most relevant explanation in Bayesian networks
Journal of Artificial Intelligence Research
Proceedings of the 2012 Symposium on Theory of Modeling and Simulation - DEVS Integrative M&S Symposium
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Bayesian networks (BNs) and influence diagrams (IDs) are probabilistic graphical models that are widely used for building diagnosis- and decision-support expert systems. Explanation of both the model and the reasoning is important for debugging these models, alleviating users' reluctance to accept their advice, and using them as tutoring systems. This paper describes some explanation options for BNs and IDs that have been implemented in Elvira and how they have been used for building medical models and teaching probabilistic reasoning to pre- and postgraduate students.