Lukas Kuhn

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.

Pervasive Diagnosis

In model-based production, a planner uses a system description to create plans that achieve production goals. The same description can be used by model-based diagnosis to infer the condition of components from sensor data. When production is realized by a sequence of plans, prior work has demonstrated that diagnosis can be used to adapt the plans to compensate for component degradation. However, the sources of diagnostic information are severely limited. Diagnosis must either make inferences from observations during production over which it has no control (passive diagnosis), or production must be halted to introduce diagnostic-specific plans (explicit diagnosis). We observe that the declarative nature of the model-based approach allows the planner to achieve production goals in multiple ways. This flexibility is exploited by a novel paradigm, i.e., pervasive (active) diagnosis, which constructs informative production plans that simultaneously achieve production goals while uncovering additional diagnostic information about the condition of components. We present an efficient heuristic search for these informative production plans and show through experiments on a model of an industrial digital printing press that the theoretical increase in long-run productivity can be realized on practical real-time systems. We obtain higher long-run productivity than a decoupled combination of planning and diagnosis.

The Diagnostic Competitions

The diagnostic competition (DX) provides a set of diagnostic benchmarks to evaluate diagnostic algorithms. This paper describes a common diagnostic framework used to evaluate diagnostic algorithms. This competition, started in 2009, has significantly helped shape diagnostic algorithms.