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HUMAN-MACHINE COLLABORATIVE OPTIMIZATION VIA APPRENTICESHIP SCHEDULING
HUMAN-MACHINE COLLABORATIVE OPTIMIZATION VIA APPRENTICESHIP SCHEDULING
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机译:通过学徒时间表进行人机协同优化
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摘要
Domain expert heuristics are captured within a computational framework for a task scheduling system. One or more classifiers are trained to predict (i) whether a first action should be scheduled instead of a second action using pairwise comparisons between actions scheduled by a demonstrator at particular times and actions not scheduled by the demonstrator at the particular times, and (ii) whether a particular action should be scheduled for a particular agent at a particular time. The system then generates a schedule for a set of actions to be performed by a plurality of agents using a plurality of resources over a plurality of time steps, by using the one or more classifiers to determine (i) a highest priority action in the set of actions, and (ii) whether the highest priority action should be scheduled for a particular agent at a particular time step.
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