Trading off the costs of inference vs. probing in diagnosis
This paper proposes a new algorithm which, when provided the relative costs of computation vs. probing, minimizes the total cost of diagnosis. During the diagnosis process the decision of whether to probe or to compute is dependent on the expected costs and benefits of each alternative. We base our algorithm on simple empirically derived models of costs and benefits. With these models, our algorithm operates by continuously choosing the optimum action to make next. This algorithm will not blow up on the rare pathological cases and will always (on average) find diagnoses at equal to or better cost than a conventional GDE/Sherlock.
De Kleer, J. ;Raiman, O. Trading off the costs of inference vs. probing in diagnosis. Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95); 1995 August 20-25; Montreal; Quebec; Canada. San Mateo, CA: Morgan Kaufmann for IJCAI; 1995; 2:1736-1741.