标题： 具有预测的多智能体在线问题 显示英文标题

标题： On multiagent online problems with predictions

摘要： 我们研究在多智能体设置中（竞争性）算法预测的效力。 我们引入了一个双预测器框架，该框架假设智能体使用一个预测器来预测自己的未来（自身）行为，另一个预测器来预测其他玩家的行为。 我们关注的主要问题是，在对预测器质量的各种假设下，通过使用此类预测器可以实现的最佳竞争比是什么。 作为我们框架的一个示例，我们引入并分析了滑雪租赁问题的多智能体版本。 在这个问题中，智能体可以通过 pooling 资源来获得某项资产的团体许可证。 如果未达到许可证价格，则智能体必须以单位价格每天单独租用该资产。 否则，许可证将永远对所有人免费可用。 在其他预测完美的特定情况下，遵循自身预测器的算法是最优的，但对智能体未来行为的误预测不具有鲁棒性；我们提供了一个具有更好鲁棒性特性的算法并对其进行基准测试。 显示英文摘要

摘要： We study the power of (competitive) algorithms with predictions in a multiagent setting. We introduce a two predictor framework, that assumes that agents use one predictor for their future (self) behavior, and one for the behavior of the other players. The main problem we are concerned with is understanding what are the best competitive ratios that can be achieved by employing such predictors, under various assumptions on predictor quality. As an illustration of our framework, we introduce and analyze a multiagent version of the ski-rental problem. In this problem agents can collaborate by pooling resources to get a group license for some asset. If the license price is not met then agents have to rent the asset individually for the day at a unit price. Otherwise the license becomes available forever to everyone at no extra cost. In the particular case of perfect other predictions the algorithm that follows the self predictor is optimal but not robust to mispredictions of agent's future behavior; we give an algorithm with better robustness properties and benchmark it.