Artificial Intelligence - Special issue on knowledge representation
Dynamic control of plans with temporal uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Probabilistic inference in influence diagrams
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Predictability & criticality metrics for coordination in complex environments
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Journal of Artificial Intelligence Research
Proactive algorithms for job shop scheduling with probabilistic durations
Journal of Artificial Intelligence Research
Drake: an efficient executive for temporal plans with choice
Journal of Artificial Intelligence Research
Temporal Bayesian Knowledge Bases - Reasoning about uncertainty with temporal constraints
Expert Systems with Applications: An International Journal
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In Temporal Planning a typical assumption is that the agent controls the execution time of all events such as starting and ending actions. In real domains however, this assumption is commonly violated and certain events are beyond the direct control of the plan's executive. Previous work on reasoning with uncontrollable events (Simple Temporal Problem with Uncertainty) assumes that we can bound the occurrence of each uncontrollable within a time interval. In principle however, there is no such bounding interval since there is always a non-zero probability the event will occur outside the bounds. Here we develop a new more general formalism called the Probabilistic Simple Temporal Problem (PSTP) following a probabilistic approach. We present a method for scheduling a PSTP maximizing the probability of correct execution. Subsequently, we use this method to solve the problem of finding an optimal execution strategy, i.e. a dynamic schedule where scheduling decisions can be made on-line.