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Journal Articles Journal of Artificial Intelligence Research Year : 2023

Contract Scheduling with Predictions


Contract scheduling is a general technique that allows to design a system with interruptible capabilities, given an algorithm that is not necessarily interruptible. Previous work on this topic has largely assumed that the interruption is a worst-case deadline that is unknown to the scheduler. In this work, we study the setting in which there is a potentially erroneous prediction concerning the interruption. Specifically, we consider the setting in which the prediction describes the time that the interruption occurs, as well as the setting in which the prediction is obtained as a response to a single or multiple binary queries. For both settings, we investigate tradeoffs between the robustness (i.e., the worst-case performance assuming adversarial prediction) and the consistency (i.e, the performance assuming that the prediction is error-free), both from the side of positive and negative results.
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Dates and versions

hal-04032366 , version 1 (25-11-2021)
hal-04032366 , version 2 (17-03-2023)



Spyros Angelopoulos, Shahin Kamali. Contract Scheduling with Predictions. Journal of Artificial Intelligence Research, 2023, 77, pp.395-426. ⟨10.1613/jair.1.14117⟩. ⟨hal-04032366v2⟩
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