Planning and control
Anytime synthetic projection: maximizing the probability of goal satisfaction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Anytime problem solving using dynamic programming
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Planning with deadlines in stochastic domains
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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We describe a method for time-critical decision making involving sequential tasks and stochastic processes. The method employs several iterative refinement routines for solving different aspects of the decision making problem. This paper concentrates on the meta-level control problem of deliberalion scheduling, allocating computational resources to these routines. We provide different models corresponding to optimization problems that capture the different circumstances and computational strategies for decision making under time constraints. We consider precursor models in which all decision making is performed prior to execution and recurrent models in which decision making is performed in parallel with execution, accounting for the states observed during execution and anticipating future states. We describe algorithms for precursor and recurrent models and provide the results of our empirical investigations to date.