论文标题
使用地图独立的贝叶斯网络中的激励解释
Motivating explanations in Bayesian networks using MAP-independence
论文作者
论文摘要
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，诊断或分类通常被形式化为对假设变量最可能的联合价值分配的计算，鉴于证据变量的观察值（通常称为地图问题）。虽然解决地图问题给出了证据的最可能的解释，但就人用户而言，计算是一个黑匣子，并且没有提供更多的见解，使用户可以欣赏和接受该决定。例如，用户可能想知道一个未观察到的变量是否可能（在观察时）影响解释，或者在这方面是否无关紧要。在本文中，我们介绍了一个新的概念，即Map-Infivate，该概念试图捕获这种相关性的概念，并探讨了其作用，以对最佳解释的推理有理由。我们基于此概念对几个计算问题进行了正式的计算问题，并评估了它们的计算复杂性。
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.
