A model for reasoning about persistence and causation
Computational Intelligence
A survey of algorithmic methods for partially observed Markov decision processes
Annals of Operations Research
Modeling a dynamic and uncertain world I: symbolic and probabilistic reasoning about change
Artificial Intelligence
Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An algorithm for probabilistic least-commitment planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Adaptive execution in complex dynamic worlds
Adaptive execution in complex dynamic worlds
Algorithms for partially observable markov decision processes
Algorithms for partially observable markov decision processes
Planning for contingencies: a decision-based approach
Journal of Artificial Intelligence Research
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Detecting and reacting to unplanned-for world states
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Monitoring plan preconditions can allow for replanning when a precondition fails, generally far in advance of the point in the plan where the precondition is relevant. However, monitoring is generally costly, and some precondition failures have a very small impact on plan quality. We formulate a model for optimal precondition monitoring, using partially-observable Markov decisions processes, and describe methods for solving this model effectively, though approximately. Specifically, we show that the single-precondition monitoring problem is generally tractable, and the multiple-precondition monitoring policies can be effectively approximated using single-precondition solutions.