Uncertainty in Artificial Intelligence
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Learning From What You Don't Observe
Mark Peot, Ross Shachter
Abstract:
The process of diagnosis involves learning about the state of a system from various observations of symptoms or findings about the system. Sophisticated Bayesian (and other) algorithms have been developed to revise and maintain beliefs about the system as observations are made. Nonetheless, diagnostic models have tended to ignore some common sense reasoning exploited by human diagnosticians; In particular, one can learn from which observations have not been made, in the spirit of conversational implicature. There are two concepts that we describe to extract information from the observations not made. First, some symptoms, if present, are more likely to be reported before others. Second, most human diagnosticians and expert systems are economical in their data-gathering, searching first where they are more likely to find symptoms present. Thus, there is a desirable bias toward reporting symptoms that are present. We develop a simple model for these concepts that can significantly improve diagnostic inference.
Keywords: Diagnosis, implicature, belief networks, reportability, reporting bias.
Pages: 439-446
PS Link:
PDF Link: /papers/98/p439-peot.pdf
BibTex:
@INPROCEEDINGS{Peot98,
AUTHOR = "Mark Peot and Ross Shachter",
TITLE = "Learning From What You Don't Observe",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "439--446"
}


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