SGER: A Framework for Explanation in Bayesian Networks

SGER：贝叶斯网络的解释框架

• 批准号: 0842480
• 负责人: Changhe Yuan
• 金额: --
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0842480&HistoricalAwards=false
• 关键词: SGER; Framework; Explanation; Bayesian; Networks

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.

现有的基于贝叶斯网络的诊断方法通常产生未充分说明的解释，不完全考虑给定的观察，或过度指定的解释，包含不必要的变量，在解释观察。该项目通过详细阐述一个新的框架来解决这些限制，最相关的解释（MRE），用于贝叶斯网络中的解释，可以自动识别给定观测值的最相关故障。特别是，该项目包括开发和评估的近似，有效的诊断算法的基础上的MRE框架的多个故障。由于诊断很可能是贝叶斯网络最成功的应用，基于MRE的方法将通过提高医学和制造业等领域的可理解性和效率，显着改善当前的计算机辅助诊断实践。