Practical Decision Analytic Methods For Large Knowledge- Based Systems

执行诊断的专家系统越来越多地用于医学、航空航天和其他高风险领域。最近的研究表明，在这种系统中广泛使用的启发式方案来编码诊断和决策中使用的不确定知识，可能会产生错误的推理。基于概率和决策分析的方案被认为是比较合理的；然而，这些问题往往被认为难以解决。本研究的目标是发展和评估新的概率和决策分析方案，既健全又易于处理。所规划的产品包括以概率形式对不确定知识进行编码的方法；对价值、成本和偏好的判断进行编码；高效的概率推理与决策算法；以及产生系统结论的可理解解释的方法。这些技术通过应用于两个基于知识的大型系统进行了演示和评估，其中包括内科诊断系统QMR（快速医疗参考）的重新制定；以及一种用于发电的燃气轮机故障诊断与维修系统。这些技术有望导致诊断专家系统更容易构建和修改，更可靠，并产生诊断测试策略，最大限度地减少不必要和昂贵的测试。

Expert systems that perform diagnosis are being used increasingly in medicine, aerospace, and other high-stakes domains. Research has recently shown that heuristic schemes widely used in such systems to encode uncertain knowledge - used in diagnosis and decision making - can produce faulty reasoning. Schemes based on probability and decision analysis have been proposed as sounder; however, they have often then been dismissed as intractable. The goal of this research is to develop and evaluate new probabilistic and decision-analytic schemes, that are both sound and tractable. The planned products comprise methods for encoding uncertain knowledge in probabilistic form; for encoding judgments on values, costs, and preferences; efficient algorithms for probabilistic inference and decision making; and methods to generate comprehensible explanations of systems conclusions. These techniques are demonstrated and evaluated by application to two large knowledge-based systems, including a reformulation of QMR (Quick Medical Reference), a system for diagnosis in internal medicine; and a system for fault diagnosis and repair of gas turbines for power generation. These techniques are expected to lead to diagnostic expert systems that are easier to build and modify, that are more reliable, and that generate diagnostic test strategies that minimize unnecessary and expensive testing.