Constructing Diagnostic Explanations Using Schema-Structured Bayesian Networks

本研究的目标是开发一种表征诊断推理的表示和算法。问题领域的人类专家经常在特定因果假设的背景下解释该领域的数据。这种类型的推理与演绎推理相反，演绎推理从一组一般规则和事实中通过合理的推理规则推导出进一步的信息。诊断推理，通常被称为溯因推理，然而，从一组事实移动到这些事实存在的“最佳解释”。这个过程通常需要专家做出一个假设性的推测来解释事实，然后寻找可以证实这个推测的具体新信息。 因此，人类专家在观察到的信息的可能解释的空间中搜索。 本研究使用贝氏信念网路建立领域的因果模型。这种方法以精确的方式代表了因果模式的相互关系及其在解释过程中的应用。该研究支持处理相互矛盾和模棱两可的证据，以及一个明确的方法来评估似是而非的推论和学习的相对优势的条件概率从现有的统计数据的可能性。虽然研究领域是建立在数据的调查，以及在复杂的真实的时间控制的故障机制的分析分立元件半导体的故障，诊断推理是一个一般的研究领域。 其结果在医疗决策建模、集成电路故障分析以及用于真实的时间过程监测和控制中具有重要意义。 http://www.cs.unm.edu/CS_Dept/faculty/homepage/luge r

The goal of this research is to develop a representation and algorithms that characterize diagnostic reasoning. Human experts in a problem domain frequently interpret data in that domain in the context of a particular causal hypothesis. This type reasoning contrasts with deductive inference where from a set of general rules and facts further information is deduced by sound inference rules. Diagnostic reasoning, often called abductive inference, however, moves from a set of facts to the "best explanation" for the existence of these facts. This process often requires the expert to make a hypothetical conjecture that would explain the facts and then search for specific new information that can confirm that conjecture. Thus the human expert searches through a space of possible explanations for the observed information. This research uses Bayesian Belief Networks to build causal models of a domain. This approach represents in a precise way the interrelationship of causal patterns and their use in moving towards an explanation. The research supports handling of conflicting and ambiguous evidence, as well as a clear method for rating plausible inferences and the possibility of learning relative strengths of conditional probabilities from available statistical data. Although the research domain is built on data from investigation of failures of discrete component semiconductors as well as the analysis of failure mechanisms in complex real time control, diagnostic reasoning is a general research area. Results could be important in modeling medical decision-making, integrated circuit fault analysis, as well as used in real time process monitoring and control. http://www.cs.unm.edu/CS_Dept/faculty/homepage/luge r/