Defining Explanation in Probabilistic Systems
arXiv:1302.1526
Abstract
As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to explanation in the literature - one due to Gärdenfors and one due to Pearl - and show that both suffer from significant problems. We propose an approach to defining a notion of "better explanation" that combines some of the features of both together with more recent work by Pearl and others on causality.
Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)
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- Causes and Explanations: A Structural-Model Approach. Part II: Explanations
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- Most Relevant Explanation: Properties, Algorithms, and Evaluations
- Explaining Image Classifiers using Statistical Fault Localization
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