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There has long been debate about the relative merits of decision theoreticmethods and heuristic rule-based approaches for reasoning under uncertainty. Wereport an experimental comparison of the performance of the two approaches totroubleshooting, specifically to test selection for fault diagnosis. We use asexperimental testbed the problem of diagnosing motorcycle engines. The firstapproach employs heuristic test selection rules obtained from expert mechanics.We compare it with the optimal decision analytic algorithm for test selectionwhich employs estimated component failure probabilities and test costs. Thedecision analytic algorithm was found to reduce the expected cost (i.e. time)to arrive at a diagnosis by an average of 14% relative to the expert rules.Sensitivity analysis shows the results are quite robust to inaccuracy in theprobability and cost estimates. This difference suggests some interestingimplications for knowledge acquisition.

A Comparison of Decision Analysis and Expert Rules for Sequential Diagnosis