Back to the Future for Consistency-Based Trajectory Tracking
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Model-based monitoring and diagnosis of systems with software-extended behavior
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Pervasive diagnosis: the integration of diagnostic goals into production plans
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Mode estimation of model-based programs: monitoring systems with complex behavior
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Concurrent reactive plans: anticipating and forestalling execution failures
Concurrent reactive plans: anticipating and forestalling execution failures
Mini-bucket heuristics for improved search
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Today's complex production systems allow to simultaneously build different products following individual production plans. Such plans may fail due to component faults or unforeseen behavior, resulting in flawed products. In this paper, we propose a method to integrate diagnosis with plan assessment to prevent plan failure, and to gain diagnostic information when needed. In our setting, plans are generated from a planner before being executed 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 uncertain 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 discrepancies, while at the same time it can be used to predict a plan's remaining chance of success or failure. We propose 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.