Pervasive Diagnosis

In model-based control, a planner uses a system description to create a plan that achieves production goals. The same model can be used by model-based diagnosis to indirectly infer the condition of components in a system from partially informative sensors. Existing 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 plans, or production must be halted to take specific diagnostic actions. In this paper, we observe that the declarative nature of model-based control allows the planner to achieve production goals in multiple ways. We show that this flexibility can be exploited by a novel paradigm we call pervasive diagnosis which produces diagnostic production plans that simultaneously achieve production goals while generating additional information about component conditions. We derive an efficient heuristic search for these diagnostic producti...

• Type: Journal
• Year: 2010
• Where: TSMC
• Authors: Lukas Kuhn, Bob Price, Minh Binh Do, Juan Liu, Rong Zhou, Tim Schmidt, Johan de Kleer
• Keywords: Artificial Intelligence, Diagnostic Production Plans, Production Goals, Production Plans, TSMC 2010