大型出租车车队中的灵活座席可用性策略规划
Strategic Planning for Flexible Agent Availability in Large Taxi Fleets
摘要：
在大规模的多智能体系统中，如出租车车队，个体智能体（出租车司机）是自私的（最大化他们自己的利润），这可能会导致系统效率低下。其中一个低效率的问题是，在一天中的不同时间段，出租车的“所需”可用性。由于出租车司机一天可以工作有限的小时数（例如，8-10在新加坡这样的城市中，工作时间是有限的），因此需要优化具体的工作时间，以便最大限度地提高个人和社会福利。从技术上讲，这对应于解决一个大规模的多阶段自私路由游戏的过渡不确定性。解决这个问题的现有工作要么无法处理"驱动程序”约束（例如，工作时间内的休息）或不可伸缩。为此，我们提供了一种新的机制，建立在复制动力学通过行为克隆的想法。我们证明，我们的方法提供了显着更好的政策比现有的方法，提高个人代理收入和整体代理的可用性。
英文摘要：
In large-scale multi-agent systems like taxi fleets, individual agents (taxi drivers) are self-interested (maximizing their own profits) and this can introduce inefficiencies in the system. One such inefficiency is with regard to the "required" availability of taxis at different time periods during the day. Since a taxi driver can work for a limited number of hours in a day (e.g., 8-10 hours in a city like Singapore), there is a need to optimize the specific hours, so as to maximize individual as well as social welfare. Technically, this corresponds to solving a large-scale multi-stage selfish routing game with transition uncertainty. Existing work in addressing this problem is either unable to handle ``driver" constraints (e.g., breaks during work hours) or not scalable. To that end, we provide a novel mechanism that builds on replicator dynamics through ideas from behavior cloning. We demonstrate that our methods provide significantly better policies than the existing approach in terms of improving individual agent revenue and overall agent availability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
