Rational Communication in Multi-Agent Environments

We address the issue of rational communicative behavior among autonomous self-interested agents that have to make decisions as to what to communicate, to whom, and how. Following decision theory, we postulate that a rational speaker should design a speech act so as to optimize the benefit it obtains as the result of the interaction. We quantify the gain in the quality of interaction in terms of the expected utility, and we present a framework that allows an agent to compute the expected utilities of various communicative actions. Our framework uses the Recursive Modeling Method as the specialized representation used for decision-making in a multi-agent environment. This representation includes information about the agent's state of knowledge, including the agent's preferences, abilities and beliefs about the world, as well as the beliefs the agent has about the other agents, the beliefs it has about the other agents' beliefs, and so on. Decision-theoretic pragmatics of a communicative act can be then defined as the transformation the act induces on the agent's state of knowledge about its decision-making situation. This transformation leads to a change in the quality of interaction, expressed in terms of the expected utilities of the agent's best actions before and after the communicative act. We analyze decision-theoretic pragmatics of a number of important kinds of communicative acts and investigate their expected utilities using examples. Finally, we report on the agreement between our method of message selection and messages that human subjects choose in various circumstances, and show an implementation and experimental validation of our framework in a simulated multi-agent environment.     

基于博弈论的多智能体协同控制算法

为实现交通的畅通,将博弈论引入到交通控制系统中。由于当前路口交通状况只受到来自邻居路口的车辆的影响,提出了基于多智能体的分布式协同控制框架,路口智能体与邻居通过协同博弈选出最优策略进行交通控制。描述了基于博弈论的协同控制算法,并通过仿真验证了该算法能够有效的适应交通流,实现交通控制。     

Game Theory and Decision Theory in Multi-Agent Systems

In the last few years, there has been increasing interest from the agent community in the use of techniques from decision theory and game theory. Our aims in this article are firstly to briefly summarize the key concepts of decision theory and game theory, secondly to discuss how these tools are being applied in agent systems research, and finally to introduce this special issue of Autonomous Agents and Multi-Agent Systems by reviewing the papers that appear.     

基于博弈学习的多Agent城市交通协调控制

交通路口中的各Agent之间的协调问题是一个博弈问题。在有限理性的基础上,利用博弈学习思想,构建多智能体（multi-Agent）博弈学习协调算法,利用此学习协调算法对出行者行为分析并修正,实现城市交通路口的畅通,进而达到区域、全局的交通优化。最后通过实例仿真验证其可行性。     

Empowering Automated Trading in Multi-Agent Environments

Trading in the financial markets often requires that information be available in real time to be effectively processed. Furthermore, complete information is not always available about the reliability of data, or its timeliness—nevertheless, a decision must still be made about whether to trade or not. We propose a mechanism whereby different data sources are monitored, using Semantic Web facilities, by different agents, which communicate among each other to determine the presence of good trading opportunities. When a trading opportunity presents itself, the human traders are notified to determine whether or not to execute the trade. The Semantic Web, Web Services, and URML technologies are used to enable this mechanism. The human traders are notified of the trade at the optimal time so as not to either waste their resources or lose a good trading opportunity. We also have designed a rudimentary prototype system for simulating the interaction between the intelligent agents and the human beings, and show some results through experiments on this simulation for trading of the Chicago Board Options Exchange (CBOE) options.     

Reflections on the Nature of Multi-Agent Coordination and Its Implications for an Agent Architecture

The development of enabling infrastructure for the next generation of multi-agent systems consisting of large numbers of agents and operating in open environments is one of the key challenges for the multi-agent community.Current infrastructure support does not materially assist in the development of sophisticated agent coordination strategies. It is the need for and the development of such a high-level support structure that will be the focus of this paper. A domain-independent (generic) agent architecture is proposed that wraps around an agent's problem-solving component in order to make problem solving responsive to real-time constraints, available network resources, and the need to coordinate—both in the large and small—with problem-solving activities of other agents. This architecture contains five components, local agent scheduling, multi-agent coordination, organizational design, detection and diagnosis, and on-line learning, that are designed to interact so that a range of different situation-specific coordination strategies can be implemented and adapted as the situation evolves. The presentation of this architecture is followed by a more detailed discussion on the interaction among these components and the research questions that need to be answered to understand the appropriateness of this architecture for the next generation of multi-agent systems.     

一般军事决策过程的协调理论模型

联合作战要求参战各方克服组织、经验、资源和技能的差异,共同协作。基于协调理论的军事决策过程建模有助于理解作战活动的共同结构,有助于识别作战活动的差异及其形成差异的原因。在分析协调理论应用的基础上,根据基于协调理论的军事决策过程建模方法,使用UML2.0建立了一般军事决策过程的协调理论模型,并着重考虑了协调活动及其依赖、协调机制及其异常处理等方面的内容。模型的建立有利于分析军事决策过程中的核心任务及其依赖属性,为指挥员准确地制订和确定作战方案提供依据,从而提高军事决策过程的适应性。     

Arguments against the possibility of perfect rationality

Many different arguments against the possibility of perfect rationality have appeared in the literature, and these target several different conceptions of perfect rationality. It is not clear how these different conceptions of perfect rationality are related, nor is it clear how the arguments showing their impossibility are related, and it is especially unclear what the impossibility results show when taken together. This paper gives an exposition of the different conceptions of perfect rationality, an the various sorts of argument against them; clarifies which conceptions of perfect rationality are targeted by which arguments; and finally attempts to systematize the results available to date.     

Coordination in Competitive Environments

Despite several researches in autonomous agents important theoretical aspects of multi-agent coordination have been largely untreatable. Multiple cooperating situated agents support the promise of improved performance and increase the task allocation problems in cooperative environments. We present a general structure for coordinating heterogeneous situated agents that allows both autonomy of each agent as well as explicit coordination of them. Such situated agents are embodied for taking into account their situation to solve any action. Indeed, organizational features have been used as metaphor to achieve highest levels of interactions in an agent system. Then, a decision algorithm has been developed to perform a match between the situated agent knowledge and the requirements of an action. Finally, this paper presents preliminary results in a simulated robot soccer scenario showing an improvement of around 92% between the worst and the best cases.     

SAS和EPS多Agent体协调控制系统

本文针对SAS和EPS系统协调控制的不足,引进了多智能体(Multi-Agent)理论控制技术。详细提出半主动悬架、电动助力转向、轮胎、故障诊断等智能体的属性和特点,基于分层递阶控制方法并结合多智能体理论,建立了集成系统协调求解机制,解决半主动悬架和电动助力转向的匹配和协调控制问题,为日后车辆大系统的集成控制研究做出了尝试和铺垫。     

一种多Agent系统的社会规范

指出有关Agent社会规范研究中存在的问题,通过借鉴Coleman的社会学理论,提出一种基于权 利分配的Agent社会规范的概念.通过放宽平均权利分配的假设,给出了多Agent系统中社会规 范的形式定义,将规范的设计归结为约束满足问题.讨论了社会规范的合理性,并给出规范建 立和废除的条件.最后,针对对称利益局势讨论了社会规范的合理性,并给出了一个价值均衡 算法以改进规范的合理性,从而有助于推进基于市场的多Agent系统的研究.     

Games Machines Play

Individual rationality, or doing what is best for oneself, is a standard model used to explain and predict human behavior, and von Neumann–Morgenstern game theory is the classical mathematical formalization of this theory in multiple-agent settings. Individual rationality, however, is an inadequate model for the synthesis of artificial social systems where cooperation is essential, since it does not permit the accommodation of group interests other than as aggregations of individual interests. Satisficing game theory is based upon a well-defined notion of being good enough, and does accommodate group as well as individual interests through the use of conditional preference relationships, whereby a decision maker is able to adjust its preferences as a function of the preferences, and not just the options, of others. This new theory is offered as an alternative paradigm to construct artificial societies that are capable of complex behavior that goes beyond exclusive self interest.     

Coordination mechanisms for decentralized parallel systems

On resource sharing platforms, the execution of the jobs submitted by users is usually controlled by a centralized global scheduler. It determines efficient schedules regarding some common objective function that all organizations agree with (for instance, maximizing the utilization of the entire platform). However, in practice, each organization is mostly interested in the performance obtained for its own jobs. We study the price that the collectivity must pay in order to allow independence to selfish, self‐governing organizations, so they can choose the best schedules for their own jobs. In other words, we are interested in analyzing the costs on the global performance inflicted by the decentralization of scheduling policies. We present a game‐theoretic model for the problem and the associated coordination mechanisms developed to reduce the cost of the decentralization of the decision‐making process. The main contribution is to show (in theory and practice) how to devise pure Nash equilibria configurations for every instance of the problem and to prove that the price paid by the collectivity depends on the local scheduling policy and on the characteristics of the workload executed on such platforms. Copyright © 2014 John Wiley & Sons, Ltd.     

智能体对手建模研究进展

智能体是人工智能领域的一个核心术语。近年来,智能体技术在自动无人驾驶、机器人系统、 电子商务、传感网络、智能游戏等方面得到了广泛研究与应用。随着系统复杂性的增加,关于智能体的研究重 心由对单个智能体的研究转变为智能体间交互的研究。多个智能体交互场景中,智能体对其他智能体决策行为 的推理能力是非常重要的一个方面,通常可以通过构建参与交互的其他智能体的模型,即对手建模来实现。对 手建模有助于对其他智能体的动作、目标、信念等进行推理、分析和预测,进而实现决策优化。为此,重点关 注智能体对手建模研究,展开介绍关于智能体动作预测、偏好预测、信念预测、类型预测等方面的对手建模 技术,对其中的优缺点进行讨论和分析,并对手建模技术当前面临的一些开放问题进行总结,探讨未来可能 的研究和发展方向。     

Multi-Agent System Development Based on Organizations

Organizational models have been recently used in agent theory for modeling coordination in open systems and to ensure social order in multi-agent system applications. In this paper, we propose the employment of Organization Theory for the analysis and design of multiagent systems. Thus, we first discuss the current state of the art of organization-oriented multiagent system methods, placing emphasis on their organizational features. We also review human organizational structures, and we propose several guidelines for implementing agent organizations by means of Organization Theory. Our final aim is to employ well-known human organizational structures to develop multiagent systems.     

Coordination in a Content-Addressable Web

The World Wide Web is a very successful phenomenon, and is increasingly being used as a medium for electronic commerce rather than simple information exchange. In this paper it is argued that the low-level infrastructure of the Web is badly suited for these new applications, as well as being somewhat inefficient in its original applications; an alternative infrastructure offering better functionality and flexibility is proposed. The new infrastructure is based on the Linda coordination model, and adds a layer of abstraction over the absolute location of resources. Both theoretical and real-world issues are covered, including the problem of integrating this new system with the existing infrastructure of the Web.     

基于HLA的多agent虚拟环境表现机制

高层体系结构（HLA）是美国国防部提出的新一代仿真技术框架,它具有高度的互操作性和可重用性。为大规模、分布式的具有复杂交互行为的多agent虚拟环境提供了良好的底层框架。本文针对基于HLA／RTI规范的多agent虚拟战场环境（MAVE－HLA）,提出了基于层次结构的动态实体的多媒体表现框架和相应的行为表现机制,它支持可重用、可扩展,同时实时性和交互性易于实现。     

未知环境下分布式多机器人避碰协作算法

针对多机器人协作问题,提出一种未知环境下分布式多机器人协作避碰算法。该算法基于分布式投标模型协调多机器人运动,改进过去算法的前提假设,综合考虑机器人的实际尺寸和传感误差,通过自适应设定投标时间,提高算法的效率,针对通信延时引起的信息不一致,采用按优先级顺序进行探测的方法。仿真实验验证了该算法的可行性。     

基于扩展CNP的分布式多Agent协同系统

传统合同网协议的投标类型单一,不能准确表达参与任务分配的Agent的意图。结合应用环境的任务抢占机制需求,对合同网协议的投标类型进行适应性扩展,基于扩展合同网协议设计分布式多Agent协同系统,给出Agent程序实现方法、多Agent通信机制设计和协同机制应用实例。该系统具有良好的可扩展性,便于动态引入具有不同功能的Agent,为协同机制的研究提供了基础平台。     

基于意图的多智能体协调机制及其在Mirosot机器人

为建立一套满足Mirosot机器人足球比赛要求的新型机器人系统，提出了一种基于意图的多智能体协调机制．这种机制利用各智能体的意图，在时间和空间上加以配合，从而完成复杂行为的协调控制．将该机制用于Mirosot足球机器人系统，对该机制进行了说明．