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- W4221148436 abstract "Scheduling is the task of assigning a set of scarce resources distributed over time to a set of agents, who typically have preferences about the assignments they would like to get. Due to the constrained nature of these problems, satisfying all agents' preferences is often infeasible, which might lead to some agents not being happy with the resulting schedule. Providing explanations has been shown to increase satisfaction and trust in solutions produced by AI tools. However, it is particularly challenging to explain solutions that are influenced by and impact on multiple agents. In this paper we introduce the EXPRES framework, which can explain why a given preference was unsatisfied in a given optimal schedule. The EXPRES framework consists of: (i) an explanation generator that, based on a Mixed-Integer Linear Programming model, finds the best set of reasons that can explain an unsatisfied preference; and (ii) an explanation parser, which translates the generated explanations into human interpretable ones. Through simulations, we show that the explanation generator can efficiently scale to large instances. Finally, through a set of user studies within J.P. Morgan, we show that employees preferred the explanations generated by EXPRES over human-generated ones when considering workforce scheduling scenarios." @default.
- W4221148436 created "2022-04-03" @default.
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- W4221148436 date "2022-03-16" @default.
- W4221148436 modified "2023-09-25" @default.
- W4221148436 title "Explaining Preference-driven Schedules: the EXPRES Framework" @default.
- W4221148436 doi "https://doi.org/10.48550/arxiv.2203.08895" @default.
- W4221148436 hasPublicationYear "2022" @default.
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