共查询到19条相似文献,搜索用时 156 毫秒
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Robocup机器人足球比赛是近年来人工智能和机器人学迅速发展的一个重要的研究领域,通过这个平台,可以来评价各种理论和算法.但由于机器人足球比赛系统固有的动态性、不确定性及实时性,这就要求整个智能体团队的合作结构能够应付这种复杂环境.针对这点,文中探讨了一种基于阵型和角色的方法来实现多智能体的团队合作,通过应用到客户端程序上所取得的良好效果,证明此方法对于多智能体的团队合作是有效的. 相似文献
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多智能体足球机器人策略研究 总被引:1,自引:0,他引:1
机器人足球比赛的策略是进行机器人足球比赛的最根本的要素.通过对一个在实际仿真机器人足球比赛时使用的策略在FIRA机器人足球比赛5 VS 5仿真平台上的仿真,实现多个智能体机器人相互配合来完成进球的任务.分析了部分策略的实现方式,归纳了不同位置的智能体机器人在使用不同的策略时相互之间的协作关系.仿真结果表明多了该智能体机器人的仿真足球策略要更胜一筹. 相似文献
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本文介绍了多智能体(MAS)协调系统的发展、研究现状,以及MAS的主要研究内容和关键技术,并以机器人足球比赛的方式作为研究多智能体协调系统的典型问题进行讨论。 相似文献
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机器人足球比赛策略仿真系统的开发 总被引:10,自引:1,他引:9
多智能体系统(Multi-AgentSystem)是近来在智能机器人领域兴起的一个新课题。它主要研究多机器人在各种不利的环境条件下,如何相互配合和合作来达到某一目的。微机器人世界杯足球比赛(MIROSOT)为研究多智能体系统提供既经济又典型的实验场地。本文主要讨论机器人足球比赛所必需的比赛策略及其计算机仿真。本文首先描述了机器人足球比赛几何建模与动态建模,其次提出足球机器人的基本行为与动作仿真,最后讨论了机器人足球比赛策略及其计算机仿真。 相似文献
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郑志强 《机器人技术与应用》2004,(3):13-16
机器人足球比赛是为了给智能机器人技术和分布式人工智能研究提供一个研究与测试平台而提出的一个标准任务。从人工智能的角度来说,与用计算机对人类的象棋比赛研究相比较,机器人足球比赛将研究对象从过去计算机象棋的单智能体对单智能体发展到分布式多智能体协调对抗,将研究环境 相似文献
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基于人工神经网络的多机器人协作学习研究 总被引:5,自引:0,他引:5
机器人足球比赛是一个有趣并且复杂的新兴的人工智能研究领域,它是一个典型的多智能体系统。文中主要研究机器人足球比赛中的协作行为的学习问题,采用人工神经网络算法实现了两个足球机器人的传球学习,实验结果表明了该方法的有效性。最后讨论了对BP算法的诸多改进方法。 相似文献
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基于协作协进化的多智能体机器人协作研究 总被引:2,自引:0,他引:2
协作问题一直是自主多智能体机器人系统研究的关键问题之一。基于多智能体机器人系统的CCP协作协议所生成的各智能体机器人的任务序列依赖于目标的初始顺序,因此难以得到最优解。文章提出了利用协作协进化来实现多智能体机器人之间协作的一种机制。该方法采用基于协作种群的技术来生成多智能体机器人任务执行序列,在给定的任务分解产生的所有可能解中寻找最优解,并通过交换局部知识和并行决策等手段来优化系统的性能。利用该机制,对3个智能体协作搬运8个物体进行计算机模拟,结果表明,该机制在优化任务执行序列方面作用明显,从而能有效提高多智能体机器人系统的性能。 相似文献
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Marsella Stacy Tambe Milind Adibi Jafar Al-Onaizan Yaser Kaminka Gal A. Muslea Ion 《Autonomous Agents and Multi-Agent Systems》2001,4(1-2):115-129
Increasingly, multi-agent systems are being designed for a variety of complex, dynamic domains. Effective agent interactions in such domains raise some of the most fundamental research challenges for agent-based systems, in teamwork, multi-agent learning and agent modelling. The RoboCup research initiative, particularly the simulation league, has been proposed to pursue such multi-agent research challenges, using the common testbed of simulation soccer. Despite the significant popularity of RoboCup within the research community, general lessons have not often been extracted from participation in RoboCup. This is what we attempt to do here. We have fielded two teams, ISIS97 and ISIS98, in RoboCup competitions. These teams have been in the top four teams in these competitions. We compare the teams, and attempt to analyze and generalize the lessons learned. This analysis reveals several surprises, pointing out lessons for teamwork and for multi-agent learning. 相似文献
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Milind Tambe Jafar Adibi Yaser Al-Onaizan Ali Erdem Gal A. Kaminka Stacy C. Marsella Ion Muslea 《Artificial Intelligence》1999,110(2):215
Multi-agent collaboration or teamwork and learning are two critical research challenges in a large number of multi-agent applications. These research challenges are highlighted in RoboCup, an international project focused on robotic and synthetic soccer as a common testbed for research in multi-agent systems. This article describes our approach to address these challenges, based on a team of soccer-playing agents built for the simulation league of RoboCup—the most popular of the RoboCup leagues so far.To address the challenge of teamwork, we investigate a novel approach based on the (re)use of a domain-independent, explicit model of teamwork, an explicitly represented hierarchy of team plans and goals, and a team organization hierarchy based on roles and role-relationships. This general approach to teamwork, shown to be applicable in other domains beyond RoboCup, both reduces development time and improves teamwork flexibility. We also demonstrate the application of off-line and on-line learning to improve and specialize agents' individual skills in RoboCup. These capabilities enabled our soccer-playing team, ISIS, to successfully participate in the first international RoboCup soccer tournament (RoboCup'97) held in Nagoya, Japan, in August 1997. ISIS won the third-place prize in over 30 teams that participated in the simulation league. 相似文献
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Task decomposition, dynamic role assignment, and low-bandwidth communication for real-time strategic teamwork 总被引:6,自引:0,他引:6
Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team synchronization (PTS) domains as time-critical environments in which agents act autonomously with low communication, but in which they can periodically synchronize in a full-communication setting. The two main contributions of this article are a flexible team agent structure and a method for inter-agent communication. First, the team agent structure allows agents to capture and reason about team agreements. We achieve collaboration between agents through the introduction of formations. A formation decomposes the task space defining a set of roles. Homogeneous agents can flexibly switch roles within formations, and agents can change formations dynamically, according to pre-defined triggers to be evaluated at run-time. This flexibility increases the performance of the overall team. Our teamwork structure further includes pre-planning for frequently occurring situations. Second, the communication method is designed for use during the low-communication periods in PTS domains. It overcomes the obstacles to inter-agent communication in multi-agent environments with unreliable, single-channel, high-cost, low-bandwidth communication. We fully implemented both the flexible teamwork structure and the communication method in the domain of simulated robotic soccer, and conducted controlled empirical experiments to verify their effectiveness. In addition, our simulator team made it to the semi-finals of the RoboCup-97 competition, in which 29 teams participated. It achieved a total score of 67–9 over six different games, and successfully demonstrated its flexible teamwork structure and inter-agent communication. 相似文献
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机器人足球比赛是一个有趣且复杂的新兴人工智能研究领域,为人工智能和多智能体合作的理论发展提供了一个重要的实验平台,并使多智能体之间的合作、控制等许多新的理论和算法能够在其中得以测试和发展。本文通过对足球机器人传球策略进行分析提出了改进方法,以期提高传球决策的成功率。 相似文献
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多智能体强化学习及其在足球机器人角色分配中的应用 总被引:2,自引:0,他引:2
足球机器人系统是一个典型的多智能体系统, 每个机器人球员选择动作不仅与自身的状态有关, 还要受到其他球员的影响, 因此通过强化学习来实现足球机器人决策策略需要采用组合状态和组合动作. 本文研究了基于智能体动作预测的多智能体强化学习算法, 使用朴素贝叶斯分类器来预测其他智能体的动作. 并引入策略共享机制来交换多智能体所学习的策略, 以提高多智能体强化学习的速度. 最后, 研究了所提出的方法在足球机器人动态角色分配中的应用, 实现了多机器人的分工和协作. 相似文献
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为了更好地解决一类通讯受限环境中多智能体任务协作规划问题,提出了基于MAXQ-OP的多智能体在线规划方法,并在RoboCup仿真2D足球比赛的人墙站位和多球员传球问题中对算法进行了实验.实验结果表明,这个方法使智能体在需要协作配合的环境中的表现比传统方法有了明显提升. 相似文献
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机器人足球比赛决策程序的图形化编程 总被引:3,自引:0,他引:3
对机器人足球比赛决策程序的多数研究者而言,主要研究多智能体系统(MAS)及其协作问题,采用算法、编程技巧均较复杂。为了在青少年中开展机器人足球比赛,必须为他们提供一种简单易用、趣味直观的决策程序编程方法。论文首先描述了机器人足球比赛决策程序的一般结构,以及产生式推理模型和决策的表达方式,并在此基础上得出通用机器人足球比赛决策程序流程图。最后提出一种直观的图形化比赛决策程序编程方法,编程者只需要改变图形的属性就可以修改比赛决策程序,降低机器人足球比赛决策程序编程的门槛。 相似文献
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