Decision-theoretic case-based reasoning
Breese, J.S.; Heckerman, D.
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
Volume 26, Issue 6, Nov 1996 Page(s):838 - 842
Digital Object Identifier 10.1109/3468.541343
Summary:We describe a decision-theoretic methodology for case-based
reasoning in diagnosis and troubleshooting applications. The system
utilizes a special-structure Bayesian network to represent diagnostic
cases, with nodes representing issues, causes, and symptoms. Dirichlet
distributions are assessed at knowledge acquisition time to indicate the
strength of relationships between variables. During a diagnosis session,
a relevant subnetwork is extracted from a Bayesian-network database that
describes a very large number of diagnostic interactions and cases. The
constructed network is used to make recommendations regarding possible
repairs and additional observations, based on an estimate of expected
repair costs. As cases are resolved, observations of issues, causes,
symptoms, and the success of repairs are recorded. New variables are
added to the database, and the probabilities associated with variables
already in the database are updated. In this way, the inferential
behavior of system adjusts to the characteristics of the target
population of users. We show how these elements work together in a cycle
of troubleshooting tasks, and describe some results from a pilot system
implementation and deployment
View citation and abstract |
Cart
