As Bayesian Networks and Influence Diagrams are being used more and more widely, the importance of an efficient explanation mechanism becomes more apparent. We focus on predictive explanations, the ones designed to explain predictions and recommendations of probabilistic systems. We analyze the issues involved in defining, computing and evaluating such explanations and present an algorithm to compute them. Introduction As knowledge-based reasoning systems begin addressing real-world problems, they are often designed to be used not by experts but by people unfamiliar with the domain. Such people are unlikely to accept system's prediction or advice without some explanation. In addition, the systems' ever increasing size makes their computations more and more difficult to follow even for their creators. This situation makes an explanation mechanism critical for making these systems useful and widely accepted. Probabilistic systems, such as Bayesian Networks (Pearl 1988) and Influence Diagrams (Howard and Matheson 1984), need such a mechanism even more than others. Human judgment under uncertainty differs considerably from the idealized rationality of probability and decision theories.
Jan-11-2006, 10:28:59 GMT