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Subject: Seminar - Methods for treating Uncertainty in AI (CMU)
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Date: Tue, 10-Feb-87 20:20:00 EST
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Posted: Tue Feb 10 20:20:00 1987
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           Artificial Intelligence in Medicine (AIM) Seminar

                       Friday, February 13, 1987
                              1:30-4:00 PM
                               Wean 8220


           "Comparing Methods for Treating Uncertainty in AI"

                              Max Henrion

                     Engineering and Public Policy
                       Carnegie Mellon University


As schemes for representing uncertainty in expert systems proliferate, the
debate about their relative merits and drawbacks is heating up.  Current
contenders include Mycin's Certainty Factors, the Prospector scheme, Fuzzy
Logic, Dempster-Shafer Theory, qualitative/verbal approaches, and a variety
of coherent probabilistic schemes, including Bayesian belief nets, influence
diagrams, and Maximum Entropy approaches.  I will discuss various criteria
for comparing them, including epistemological (do they represent what we mean
by "uncertainty"?), heuristic (Are they computationally practical? Are they
"good enough"?), and transductional (Can you easily encode human judgment and
can you explain the results?).  I will examine treatment of dependent
evidence, causal and diagnostic reasoning, with simple medical examples.  I
will also describe a recent experiment comparing knowledge engineering for a
rule-based expert system  with a decision analysis/Bayes' net approach to the
same task.

Papers available from Max Henrion (maxh@Andrew)