Defining Explanation in Probabilistic Systems

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 G\" ardenfors 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.

Authors

• Urszula Chajewska
• Joseph Y. Halpern

Keywords

• Explanation
• causality
• Bayesian networks