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放大的多智能体规划:一个最好的响应的方法

Scaling Up Multiagent Planning: A Best-Response Approach
课程网址: http://videolectures.net/icaps2011_jonsson_planning/  
主讲教师: Anders Jonsson
开课单位: 庞培法布拉大学
开课时间: 2011-07-21
课程语种: 英语
中文简介:
由于不同代理的并发动作引起的动作空间中的指数爆炸,多智能体规划在一般情况下计算困难。同时,许多情景需要计算对自利代理人具有战略意义的计划,以确保有足够的激励使这些代理人参与联合计划。在本文中,我们提出了一种多智能体规划和计划改进方法,该方法基于使用标准单代理规划算法进行迭代最佳响应规划。在与拥挤游戏相对应的约束类型的规划方案中,这保证会收敛到关于代理商的纳什均衡的计划。整个计划空间的首选项配置文件。然而,我们超出这些受限情景的经验评估表明,该算法作为一般多智能体规划问题的计划改进方法具有更广泛的适用性。各种领域的广泛经验实验说明了我们的方法在两种情况下的可扩展性。
课程简介: Multiagent planning is computationally hard in the general case due to the exponential blowup in the action space induced by concurrent action of different agents. At the same time, many scenarios require the computation of plans that are strategically meaningful for selfinterested agents, in order to ensure that there would be sufficient incentives for those agents to participate in a joint plan. In this paper, we present a multiagent planning and plan improvement method that is based on conducting iterative best-response planning using standard single-agent planning algorithms. In constrained types of planning scenarios that correspond to congestion games, this is guaranteed to converge to a plan that is a Nash equilibrium with regard to agents’ preference profiles over the entire plan space. Our empirical evaluation beyond these restricted scenarios shows, however, that the algorithm has much broader applicability as a method for plan improvement in general multiagent planning problems. Extensive empirical experiments in various domains illustrate the scalability of our method in both cases.
关 键 词: 多智能体规划; 指数爆破; 纳什均衡
课程来源: 视频讲座网
最后编审: 2020-06-27:zyk
阅读次数: 45