Pervasive Diagnosis: The Integration of Diagnostic Goals into Production Plans

In model-based control, a planner uses a system description to create a plan that achieves production goals (Fikes & Nilsson 1971). The same description can be used by model-based diagnosis to infer the condition of components in a system from partially informative sensors. Prior work has demonstrated that diagnosis can be used to adapt the control of a system to changes in its components. However diagnosis must either make inferences from passive observations of production, or production must be halted to take diagnostic actions. We observe that the declarative nature of model-based control allows the planner to achieve production goals in multiple ways. This exibility can be exploited with a novel paradigm we call pervasive diagnosis which produces diagnostic production plans that simultaneously achieve production goals while uncovering additional information about component health. We present an efficient heuristic search for these diagnostic production plans and show through experiments on a model of an industrial digital printing press that the theoretical increase in information can be realized on practical real-time systems. We obtain higher long-run productivity than a decoupled combination of planning and diagnosis.