论文标题
有效的资源分配，并在不安的多军匪徒中限制公平限制
Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits
论文作者
论文摘要
躁动不安的多武器匪（RMAB）是一种恰当的模型，可以代表公共卫生干预措施（例如结核病，母性和儿童保育），反竞争计划，传感器监测，个性化建议等方面的决策问题。 RMAB的现有研究为各种环境提供了机制和理论结果，其中重点是最大化期望值。在本文中，我们有兴趣确保RMAB决策对不同的武器也很公平，同时最大程度地提高了预期价值。在公共卫生环境的背景下，这将确保在做出公共卫生干预决策时公平地代表不同的人和/或社区。为了实现这一目标，我们正式定义了RMAB中的公平约束，并提供了计划和学习方法以公平的方式解决RMAB。我们证明了公平RMAB的关键理论特性，并在实验上证明了我们所提出的方法处理公平限制，而无需在溶液质量上牺牲。
Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide variety of settings, where the focus is on maximizing expected value. In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value. In the context of public health settings, this would ensure that different people and/or communities are fairly represented while making public health intervention decisions. To achieve this goal, we formally define the fairness constraints in RMAB and provide planning and learning methods to solve RMAB in a fair manner. We demonstrate key theoretical properties of fair RMAB and experimentally demonstrate that our proposed methods handle fairness constraints without sacrificing significantly on solution quality.
