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Getting the probabilities right for measurement selection

 

The core objective of model-based diagnosis is to identify candidate diagnoses which explain the observed symptoms. Usually there are multiple such diagnoses and a model-based diagnostic engine proposes additional measurements to better isolate the actual diagnosis. An objective of such an algorithm is to identify this diagnosis in minimum average expected cost (e.g., the sum of the costs of the measurements). Minimizing this cost requires having accurate probability estimates for the candidate diagnoses. Most diagnostic engines utilize sequential diagnosis combined with Bayes Rule to determine the posterior probability of a candidate diagnosis given a measurement outcome. Unfortunately, one of the terms of Bayes rule, the conditional probability of an measurement outcome given a candidate diagnosis, must often be estimated (noted as $epsilon$ in most formulations). This paper presents a reformulation of the sequential diagnosis process used in most diagnostic engines and shows how different $epsilon$ policies lead to varying results.

 
citation

de Kleer, J. Getting the probabilities right for measurement selection. 17th International Workshop on Principles of Diagnosis (DX2006); 2006 June 26-28; Peñaranda de Duero; Burgos; Spain.