Publication Date:
2019-06-28
Description:
This paper describes DTS, a decision-theoretic scheduler designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems, and using probabilistic inference to aggregate this information in light of features of a given problem. BPS, the Bayesian Problem-Solver, introduced a similar approach to solving single-agent and adversarial graph search problems, yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.
Keywords:
CYBERNETICS
Type:
NASA. Ames Research Center, Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning; p 67-71
Format:
application/pdf
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