Decision-Theoretic Case-Based Reasoning

John S. Breese, David Heckerman
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:56-63, 1995.

Abstract

We describe a decision-theoretic methodology for case-based reasoning in diagnosis and troubleshooting applications. The system utilizes a special-structure belief 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 belief-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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-breese95a, title = {Decision-Theoretic Case-Based Reasoning}, author = {Breese, John S. and Heckerman, David}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {56--63}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/breese95a/breese95a.pdf}, url = {https://proceedings.mlr.press/r0/breese95a.html}, abstract = {We describe a decision-theoretic methodology for case-based reasoning in diagnosis and troubleshooting applications. The system utilizes a special-structure belief 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 belief-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.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Decision-Theoretic Case-Based Reasoning %A John S. Breese %A David Heckerman %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-breese95a %I PMLR %P 56--63 %U https://proceedings.mlr.press/r0/breese95a.html %V R0 %X We describe a decision-theoretic methodology for case-based reasoning in diagnosis and troubleshooting applications. The system utilizes a special-structure belief 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 belief-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. %Z Reissued by PMLR on 01 May 2022.
APA
Breese, J.S. & Heckerman, D.. (1995). Decision-Theoretic Case-Based Reasoning. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:56-63 Available from https://proceedings.mlr.press/r0/breese95a.html. Reissued by PMLR on 01 May 2022.

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