MVILAIN@G.BBN.COM (Marc Vilain) (06/11/88)
Date: Fri, 10 Jun 88 13:52 EDT
From: Marc Vilain <MVILAIN@G.BBN.COM>
Subject: BBN AI Seminar -- Phylis Koton
To: ai-folks@G.BBN.COM
BBN Science Development Program
AI Seminar Series Lecture
MODEL-BASED DIAGNOSTIC REASONING USING PAST EXPERIENCES
Phylis Koton
MIT Lab for Computer Science
(ELAN@XX.LCS.MIT.EDU)
BBN Labs
10 Moulton Street
2nd floor large conference room
10:30 am, Tuesday June 14
The problem-solving performance of most people improves with experience.
The performance of most expert systems does not. People solve
unfamiliar problems slowly, but recognize and quickly solve problems
that are similar to those they have solved before. People also remember
problems that they have solved, thereby improving their performance on
similar problems in the future. This talk will describe a system,
CASEY, that uses case-based reasoning to recall and remember problems it
has seen before, and uses a causal model of its domain to justify
re-using previous solutions and to solve unfamiliar problems.
CASEY overcomes some of the major weaknesses of case-based reasoning
through its use of a causal model of the domain. First, the model
identifies the important features for matching, and this is done
individually for each case. Second, CASEY can prove that a retrieved
solution is applicable to the new case by analyzing its differences from
the new case in the context of the model. CASEY overcomes the speed
limitation of model-based reasoning by remembering a previous similar
case and making small changes to its solution. It overcomes the
inability of associational reasoning to deal with unanticipated problems
by recognizing when it has not seen a similar problem before, and using
model-based reasoning in those circumstances.
The techniques developed for CASEY were implemented in the domain of
medical diagnosis, and resulted in solutions identical to those derived
by a model-based expert system for the same domain, but with an increase
of several orders of magnitude in efficiency. Furthermore, the methods
used by the system are domain-independent and should be applicable in
other domains with models of a similar form.
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