城市出租车服务中的大规模代理指导

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摘要翻译：
在城市的士服务的背景下，空载的士巡游是一种资源浪费。在这项工作中，我们力求尽量减少这种浪费。通过对大量出租车运营轨迹的分析发现，出租车服务的低效是由司机贪婪的巡游行为造成的。我们将现有系统建模为连续时间马尔可夫链。为了解决这个问题，我们建议每辆出租车都配备一个智能代理，在巡游乘客时引导司机。然后，借鉴人工智能中关于多智能体规划的文献，我们探索了两种可能的方法来计算这种指导。第一个公式假定完全合作的驱动因素。原则上，这使得我们可以计算系统范围内的最优巡航策略。这被建模为一个马尔可夫决策过程。第二个公式假定理性的驱动者，寻求最大化他们自己的利润。这被建模为一个随机拥挤博弈，一个特殊的随机博弈。纳什均衡政策被提出作为博弈的解决方案，其中没有驱动者有动机单独偏离它。实证结果表明，两种方案都显著提高了服务效率。
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英文标题：
《Toward Large-Scale Agent Guidance in an Urban Taxi Service》
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作者：
Lucas Agussurja, Hoong Chuin Lau
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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英文摘要：
  Empty taxi cruising represents a wastage of resources in the context of urban taxi services. In this work, we seek to minimize such wastage. An analysis of a large trace of taxi operations reveals that the services' inefficiency is caused by drivers' greedy cruising behavior. We model the existing system as a continuous time Markov chain. To address the problem, we propose that each taxi be equipped with an intelligent agent that will guide the driver when cruising for passengers. Then, drawing from AI literature on multiagent planning, we explore two possible ways to compute such guidance. The first formulation assumes fully cooperative drivers. This allows us, in principle, to compute systemwide optimal cruising policy. This is modeled as a Markov decision process. The second formulation assumes rational drivers, seeking to maximize their own profit. This is modeled as a stochastic congestion game, a specialization of stochastic games. Nash equilibrium policy is proposed as the solution to the game, where no driver has the incentive to singly deviate from it. Empirical result shows that both formulations improve the efficiency of the service significantly.
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PDF链接：
https://arxiv.org/pdf/1210.4849

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关键词：租车服务 大规模 出租车 Intelligence Applications 博弈 司机 出租车 建模 指导