SGER: A Framework for Explanation in Bayesian Networks

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Existing Bayesian network-based diagnostic approaches often produce either underspecified explanations that do not fully account for given observations, or overspecified explanations that contain variables unnecessary in explaining the observations. This project addresses these limitations by elaborating a new framework, Most Relevant Explanation (MRE), for explanation in Bayesian networks that can automatically identify the most relevant fault(s) for given observations. In particular, the project includes development and evaluation of approximate, efficient diagnostic algorithms for multiple faults based on the MRE framework. Because diagnosis is quite likely the most successful application of Bayesian networks, MRE-based methodologies will significantly improve the current practice of computer-aided diagnosis by improving the comprehensibility and efficiency in areas such as medicine and manufacturing.