Motivating explanations in Bayesian networks using MAP-independence

J. Kwisthout
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引用次数: 2

Abstract

In decision support systems the motivation and justification of the system's diagnosis or classification is crucial for the acceptance of the system by the human user. In Bayesian networks a diagnosis or classification is typically formalized as the computation of the most probable joint value assignment to the hypothesis variables, given the observed values of the evidence variables (generally known as the MAP problem). While solving the MAP problem gives the most probable explanation of the evidence, the computation is a black box as far as the human user is concerned and it does not give additional insights that allow the user to appreciate and accept the decision. For example, a user might want to know to whether an unobserved variable could potentially (upon observation) impact the explanation, or whether it is irrelevant in this aspect. In this paper we introduce a new concept, MAP- independence, which tries to capture this notion of relevance, and explore its role towards a potential justification of an inference to the best explanation. We formalize several computational problems based on this concept and assess their computational complexity.
基于map独立性的贝叶斯网络激励解释
在决策支持系统中,系统诊断或分类的动机和理由对于人类用户接受系统至关重要。在贝叶斯网络中,诊断或分类通常形式化为给定证据变量的观测值,计算假设变量的最可能联合值分配(通常称为MAP问题)。虽然解决MAP问题提供了对证据的最可能的解释,但就人类用户而言,计算是一个黑盒子,它没有提供允许用户欣赏和接受决策的额外见解。例如,用户可能想知道一个未观察到的变量是否可能(根据观察)影响解释,或者它在这方面是否无关。在本文中,我们引入了一个新的概念,MAP-独立性,它试图捕捉到相关性的概念,并探讨了它在对最佳解释的推理进行潜在证明方面的作用。我们基于这个概念形式化了几个计算问题,并评估了它们的计算复杂性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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