Pervasive diagnosis

Abstract

In model-based production, a planner uses a system description to create a plan that achieves production goals. 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 production to changes in its components. However, diagnosis must either make inferences from passive observations during production, or production must be halted to take diagnostic actions. 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 with a novel paradigm we call pervasive diagnosis which constructs informative production plans that simultaneously achieve production goals while uncovering additional 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.