Uncertainty in Artificial Intelligence
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Deliberation Scheduling for Time-Critical Sequential Decision Making
Thomas Dean, Leslie Kaelbling, Jak Kirman, Ann Nicholson
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 deliberation 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.
Pages: 309-316
PS Link:
PDF Link: /papers/93/p309-dean.pdf
AUTHOR = "Thomas Dean and Leslie Kaelbling and Jak Kirman and Ann Nicholson",
TITLE = "Deliberation Scheduling for Time-Critical Sequential Decision Making",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "309--316"

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