Special Joint Session of
Decision Analysis Seminar and
Seminar on Computational Learning and Adaptation
Learning From What You DON'T Observe
Professor Ross Shachter
Engineering, Economic Systems & Operations Research
Stanford University
The process of diagnosis involves learning about the state of a system
from various observations of symptoms or findings about the system.
Sophisticated Bayesian 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.
(joint work with Mark A. Peot)
Date: Thurs., September 24; Time: 4:30-5:45PM; Place: Terman 332
The goal of the Computational Learning Seminar is to increase
communication among local researchers with interests in computational
approaches to learning and adaptation. If you would like to be added
to (or removed from) the mailing list, or if you are interested in
giving a talk in the seminar, please send email to
iba@isle.org.
Return to learning seminar schedule.