A unified information criterion for evaluating probe and test selection

Diagnostic tasks often need to make the decision of what measurement to make or what action to take in order to resolve ambiguities in diagnosis. Intuitively one would like to seek the most ldquoinformativerdquo choice. In the paper, we formalize this intuition and propose an information criterion for evaluating and comparing measurement/action choices based on their information contribution. The criterion is mutual information, an information-theoretic concept measuring statistical dependence. The information criterion gives a precise quantitative metric to differentiate the quality of measurement/action choices. We use a few concrete example in two separate paradigms, probe selection in circuit diagnosis and test generation in production plants, to illustrate the mutual information criterion. Despite the apparent differences of the two paradigms, the information criterion works coherently. We demonstrate how different probing actions or test plans vary in their information values.