基于本体的多Agent交互模型

介绍Ontology、Agent及Multi-agent基本概念,阐述了MAS交互和本体在MAS交互中的应用,提出一种基于本体的MAS交互模型图,并详细解释了模型中各组成部分的功能。     

基于多Agent的新型变电站通信的研究

通信在电力系统中起到举足轻重的作用，通信不仅涉及信息的传输，关系到是否能正确地处理故障，而且更是关系到整个系统的安全。本文在介绍Agent的基础上，提出一个基于多Agent的变电站自动化系统，并对其中所涉及到的通信细节进行了相关讨论。     

基于BICC的多Agent通信

一、引言 Agent是计算机科学领域中一个新兴概念,它是指在特定环境下具有自主性、社会性和主动性等特征的智能主体。多Agent系统是由一组具有一定资源和能力、相对独立且相互作用的Agent组成的。多Agent通信是分布人工智能和多Agent系统研究的核心。在     

基于语义Web的本体与多Agent集成研究

语义Web服务是在分布式环境下构建复杂系统的一种新兴技术.阐述了语义Web中本体的核心概念和多Agent系统的主要特点.在此基础上,提出了基于本体的多Agent系统的体系模型,并探讨了该架构对于构建开放的、动态的、异构的多Agent系统所具有的优越性.     

基于流演算的多Agent通信动作的研究

为扩展多Agent系统中Agents之间的交互特性,在流演算理论框架下研究了多Agent的通信动作.根据流演算理论和主体通信语言ACL,结合其它Agent的知识表示,引入了通信动作,给出了形式化描述,并讨论了通信动作如何影响通信双方的知识状态,并通过一个智能日程安排实例验证了上述理论,为构建适应动态.不完全可知环境下复杂的多主体系统提供了理论基础.     

资源受限的多Agent系统通信研究

在部分可观察环境中,Agent需要知识共享完成分布式合作.在资源受限的条件下,不加选择的通信会因为消耗有用的带宽产生性能瓶颈.因此,在资源受限的系统中需要在通信效用和通信代价之间进行适当的折衷.通过对无线网络通信方法的研究,基于信息冗余的思想讨论了多Agent系统的通信问题,提出了选择性信息交换算法,保证了Agent之间必要时的通信,以此来提高合作的效率.     

一个基于XML的Agent通信框架

Agent通信是多Agnet系统的核心活动之一,是Agent相互协调、合作、竞争和完成任务的基础。Agent通信语言为此提供了一种高层次、灵活、通用的通信模式。在KQML和FIPA－ACL的基础上提出通用Agent通信语言的概念,阐述构建通用ACL的重要性和可行性,深入讨论一个基于XML的Agent通信框架,最后给出实例。     

多Agent系统DOLTRI—Agent中的通信及其安全机制

本文介绍了一个基于Internet的信息检索多Agent系统DOLTRI－Agent内多个Agent间的通信框架及其安全机制。主要论述各个Agent间相互通信所采用的通信语言,通信协议以及信息交换和相互合作的方法,在此基础上提出了一种多Agent系统安全通信模型。     

基于FIPA的多Agent系统通信研究

Agent通信是多Agent系统(MAS:Multi-Agent System)的核心活动之一,是Agent相互协调、合作的基础。依据FIPA(Foun-dation for Intelligent Physical Agents)规范,采用消息传送与CORBA技术相结合的通信方式,实现了MAS中Agent之间的有效通信。通过具体的通信实例讨论了MAS中Agent通信的实现。     

Agent通信机制探讨

Agent通信是多Agent系统的核心活动之一,是Agent相互协调、合作、竞争和完成任务的基础。Agent通信语言为此提供了一种高层次的、灵活、通用的通信模式。基于目前的KQML和FIPA-ACL,文章提出通用Agent通信语言的概念,给出一个递进式Agent通信模型和相应的语义描述,力图促进Agent通信的标准化和提高Agent通信的能力和效率。     

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.     

When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning

Abstract

Multi-agent systems need to communicate to coordinate a shared task. We show that a recurrent neural network (RNN) can learn a communication protocol for coordination, even if the actions to coordinate are performed steps after the communication phase. We show that a separation of tasks with different temporal scale is necessary for successful learning. We contribute a hierarchical deep reinforcement learning model for multi-agent systems that separates the communication and coordination task from the action picking through a hierarchical policy. We further on show, that a separation of concerns in communication is beneficial but not necessary. As a testbed, we propose the Dungeon Lever Game and we extend the Differentiable Inter-Agent Learning (DIAL) framework. We present and compare results from different model variations on the Dungeon Lever Game.     

基于本体的语义Web服务通信协议的研究

在简单介绍语义W eb服务、本体的同时,将本体与通信协议结合,提出建立通信协议本体的概念,并给出协议本体的简单模型。将语义W eb服务中的核心SOAP协议引入到协议本体中,在服务机制中将语义引入消息传输机制,提高服务的灵活性以及适应性。还可减少事务处理中产生的不确定性。     

多智能体机器人系统分散式通信决策研究

为了减少多智能体机器人系统协调所需通信的数量,提出了一种新的方法.利用有向无环图表示团队的可能联合信度,并基于此以分散式的方式制定通信决策,仅当智能体自身的观察信息显示共享信息将导致期望回报升高时才选择通信.通过维持以及推理团队的可能联合信度将集中式单智能体策略应用于分散式多智能体POM-DP问题.通过实验以及一个详细的实例表明,本文方法能够有效地减少通信资源的使用,同时提高分散执行的性能.     

一种多Agent多议题的协商模型

协商是多Agent系统实现协作、协调和冲突消解的关键技术。本文分析了协商问题的实质和协商过程,提出了一种支持多轮协商的多Agent多议题协商模型。模型中引入了Agent类型的概念,在信息不完全的条件下,协商Agent通过推测协商对手的类型来指导自身的提议策略和协商战术,使提议更具针对性,避免了盲目性,从而节约了协商时间,提高了协
商质量。     

Secure Consensus Control for Multi-Agent Systems With Attacks and Communication Delays

This paper addresses the consensus problem for nonlinear multi-agent systems suffering from attacks and communication delays. The network studied in this paper consists of two types of agents, namely, loyal agents and attack agents. The loyal agents update their states based on delayed state information exchanged with their neighbors. Meanwhile, the attack agents can strategically send messages with wrong values, or collude with other attack agents to disrupt the correct operation of the system. We design a novel delay robust secure consensus (DRSC) algorithm according to the neighboring nodes' delayed information. Convergence analysis of the system under the protocol designed is provided by using Lyapunov-Krasovskii stability theory and Barbalat-like argument approach. Finally, an example and simulation results are presented to demonstrate the effectiveness of the algorithm.      

多Agent系统问题求解的广义粒子模型方法

提出一种多Agent系统分布式问题求解的新的广义粒子模型,将复杂环境下多Agent系统资源分配和任务规划的优化问题转变为广义粒子模型中的粒子运动学和动力学问题.广义粒子模型可以描述和处理的复杂环境包括多Agent系统中的Agent之间存在的随机、并发、多类型的交互行为.各Agent有不同的个性、自治性、生命周期、拥塞程度和故障几率等.本文讨论了广义粒子模型和多Agent系统分布式问题求解的关系,提出了广义粒子模型的数学物理模型和多Agent系统分布式问题求解算法,并且证明了它们的正确性、收敛性、稳定平衡性等基本性质.通过复杂环境下多Agent系统资源分配和任务规划问题的实验和比较,证实了广义粒子模型方法的有效性及其特点.     

Learning to Share Meaning in a Multi-Agent System

The development of the semantic Web will require agents to use common domain ontologies to facilitate communication of conceptual knowledge. However, the proliferation of domain ontologies may also result in conflicts between the meanings assigned to the various terms. That is, agents with diverse ontologies may use different terms to refer to the same meaning or the same term to refer to different meanings. Agents will need a method for learning and translating similar semantic concepts between diverse ontologies. Only until recently have researchers diverged from the last decade's common ontology paradigm to a paradigm involving agents that can share knowledge using diverse ontologies. This paper describes how we address this agent knowledge sharing problem of how agents deal with diverse ontologies by introducing a methodology and algorithms for multi-agent knowledge sharing and learning in a peer-to-peer setting. We demonstrate how this approach will enable multi-agent systems to assist groups of people in locating, translating, and sharing knowledge using our Distributed Ontology Gathering Group Integration Environment (DOGGIE) and describe our proof-of-concept experiments. DOGGIE synthesizes agent communication, machine learning, and reasoning for information sharing in the Web domain.     

基于多智能体通讯机制的多机器人编队系统

源于分布式人工智能的多智能体系统,以其突出的灵活性和适用性,被应用于多机器人协调系统领域。论文从多智能体理论出发,研究在真实世界里利用多协议全双工的通讯机制如何来实现多智能体机器人系统的技术和方法,并通过编队试验对系统性能进行验证。结果表明系统的构建是稳定可行的。     

基于本体的工作流元模型研究

提出使用本体来描述工作流元模型,通过综合目前在工作流元模型方面的研究成果,提炼出工作流元模型的概念,建立可扩展、可理解、可维护的工作流元模型本体,实现在模型层次上的扩展,同时建立工作流的本体知识库,为以后的工作流模型的分析建模、模型的自动验证、以及工作流管理系统之间的互操作奠定坚实的基础。