Today’s complex production systems allow to simulta- neously build different products following individual pro- duction plans. Such plans may fail due to component faults or unforeseen behavior, resulting in flawed prod- ucts. In this paper, we propose a method to integrate di- agnosis with plan assessment to prevent plan failure, and to gain diagnostic information when needed. In our set- ting, plans are generated from a planner before being ex- ecuted on the system. If the underlying system drifts due to component faults or unforeseen behavior, plans that are ready for execution or already being executed are uncer- tain to succeed or fail. Therefore, our approach tracks plan execution using probabilistic hierarchical constraint automata (PHCA) models of the system. This allows to explain past system behavior, such as observed discrep- ancies, while at the same time it can be used to predict a plan’s remaining chance of success or failure. We pro- pose a formulation of this combined diagnosis/assessment problem as a constraint optimization problem, and present a fast solution algorithm that estimates success or failure probabilities by considering only a limited number k of system trajectories.     «

Today’s complex production systems allow to simulta- neously build different products following individual pro- duction plans. Such plans may fail due to component faults or unforeseen behavior, resulting in flawed prod- ucts. In this paper, we propose a method to integrate di- agnosis with plan assessment to prevent plan failure, and to gain diagnostic information when needed. In our set- ting, plans are generated from a planner before being ex- ecuted on the system. If the underlying system dr...     »