Uncertainty in Artificial Intelligence
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Defining Explanation in Probabilistic Systems
Urszula Chajewska, Joseph Halpern
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 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.
Keywords: Explanation, causality, Bayesian networks.
Pages: 62-71
PS Link: HTTP://www.cs.cornell.edu/home/halpern/papers/expl.ps
PDF Link: /papers/97/p62-chajewska.pdf
BibTex:
@INPROCEEDINGS{Chajewska97,
AUTHOR = "Urszula Chajewska and Joseph Halpern",
TITLE = "Defining Explanation in Probabilistic Systems",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "62--71"
}


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