一种基于黑板模型的多Agent系统通信方法

本文以人工智能领域中的黑板模型为基础 ,提出了在 Internet环境中多 Agent系统 (MAS)的通信方法 .该方法可用于异构的 MAS系统间的通信 .首先把 MAS系统的通信环境划分为不同的层次 ,然后用黑板模型对这些层次进行了描述 .基于这种描述给出了 MAS系统通信用协议和相关参数 .而且 ,还在这些参数中设置了有关服务质量的元素 .     

基于嵌套网的JADE建模

良好的形式化建模方法是大规模开发和应用多Agent系统(MAS)的必要条件之一,使用多层的嵌套网对JADE进行层次化建模,给出面向对象的MAS与NP-nets之间的对照关系。指出通过引入层次结构,不仅能有效地控制状态空间的规模,而且能通过网托肯的嵌套方式克服Petri网结构演化的困难。     

一种基于BDI Agent的复杂系统设计建模方法

提出一种通过信念一愿望一意图(BDI)结构实现多Agent系统设计建模的方法．多Agent系统设计建模的目的是具体地模型化多Agent组织中承担不同组织职能的每一类Agent的结构，使其易于在现有的程序设计语言环境中实现，在该方法中，多Agent系统的设计建模需要建立三个模型：Agent模型、相互作用模型和相识者模型．Agent模型由信念、目标、计划三个基本子模型构成，这些子模型是根据分析阶段所获得的职能模型、协同工作过程模型以及领域本体来建立的，MAS系统中的每一Agent都是Agent模型中某一类Agent的一个实例．相互作用模型主要说明Agent之间的交互细节，如交互协议、交互语言、交互约束条件等．相识者模型说明每一类Agent的所有相识者及其属性，目前通过这一建模方法已在多智能体协同工作平台(MBOS)上开发出实际的应用系统“多智能体物资调配决策支持系统MAEDSS”。     

命令模式下的智能UUV模型设计

多智能UUV编队作战时,接收到上级的命令后,应以上级命令为中心根据环境信息采取行动完成任务。通常的Agent模式以个体意识为中心,不适合这种以命令为中心的UUV建模。本文结合近年来Agent理论的研究成果,通过对军事作战中命令体制的研究,提出一种基于命令的Agent模型组织结构,给出了该模型的形式化语言描述,并构建了基于命令机制的智能UUV模型,实现了基于MAS的多UUV机群作战仿真系统。为构建基于MAS的水下网络中心战系统提供技术支撑。     

MAS理性与模型的研究

理性是选择策略的标准,是系统内一种有序结构,关系着MAS目标的实现和MAS的稳定性。有序结构可以在平衡条件下形成（封闭系统）,也可以在远离平衡条件下形成（开放系统）,本文在讨论封闭环境下MAS理性的基础上,给出适应开放环境下的MAS理性。     

AGR元模型到组织抽象模型的建模方法

将目标驱动思想引入AGR中,提出一种从AGR元模型到多Agent系统（MAs）组织抽象模型的建模方法。对AGR进行综述,给出该方法的具体步骤和形式化过程。应用于某智能故障诊断系统的结果表明,该建模方法有利于设计人员直接利用AGR元模型构建MAS组织抽象模型,可适用于各种AGR扩展。     

基于MAS的车间管控系统中通用Agent建模与设计

在基于MAS的车间管理控制系统开发中,系统中各个Agent的建模与设计是其关键技术之一。该文提出了一种可满足MAS车间管控系统开发的通用Agent结构模型及其面向对象的表示与实现方法。基于该通用Agent的MAS系统不仅具有可集成性好、互操作性强、具有可重构能力等特点,而且可实现Agent程序的标准化,进而保障项目组协同开发的效率。     

基于多Agent系统的软件开发方法研究

面向Agent的软件工程近年来获得很多人的关注,其中MAS是一个重要的研究方向.到目前为止,已经出现了20多种MAS开发方法.本文对现有的主要方法进行了分类和总结,并利用三层次模型对常用的三类方法进行了比较,希望对读者有一定的借鉴作用.     

生态化MAS的认知模型

生态化MAS是由异构的Agent所组成的一种具有协同进化的多Agent系统。提出了生态化MAS的理念和认知模型,它是形成Agent知识和信念的基本框架;设计了一个模型检验的程序结构,并通过典型应用表明,该模型能够使Agent对环境的认知达到三个层次——概念图、模糊词计算和关联性框架。该模型能够为基于Agent的问题求解提供依据。     

基于MAS的智能决策支持系统研究

为了使多Agent系统的研究和日益强大的网络技术为建立人机一体化的智能决策支持系统提供新的途径和方法,文中在分析了多Aggent系统(MAS)体系结构的基础上,对智能决策支持系统(IDSS)进行了探讨,提出一种在网络环境下,基于MAS的智能决策支持系统模型,并给出了其决策支持过程描述、形式化定义.该模型能够为智能决策支持系统提供新的途径和方法,不仅丰富了智能决策理论,而且能够对实际决策提供有效的支持.     

MAS系统的问题求解能力分析

本文用状态空间搜索模型分析了多Agent系统(MAS)的问题求解能力，认为MAS系统中Agent之间知识的组合应用和对问题搜索方向的交互和决策是影响MAS系统问题求解能力的主要原因，在状态空间搜索模型下可以将Agent间知识的组合应用表达为不同Agent的搜索路径的组合，而Agent对搜索方向的判断是基于启发式信息做出的，从而为形式化分析MAS系统的性能建立了通用的模型．本文以A*算法为例探讨了可采纳算法下多Agent合作求解效果与Agent的知识和启发信息之间的关系，指出只有在一定条件下MAS系统才会获得更好的解题能力．本文还对非可采纳算法下MAS系统性能分析方法提出了初步看法．     

Fog computing for the integration of agents and web services in an autonomic reflexive middleware

Service-oriented architecture (SOA) has emerged as a dominant architecture for interoperability between applications, by using a weakly coupled model based on the flexibility provided by web services, which has led to a wide range of applications, which is known as cloud computing. On the other hand, multi-agent system (MAS) is widely used in the industry, because it provides an appropriate solution to complex problems, in a proactive and intelligent way. Specifically, intelligent environments (smart city, smart classroom, cyber-physical system, and smart factory) obtain great benefits by using both architectures, because MAS endows intelligence to the environment, while SOA enables users to interact with cloud services, which improve the capabilities of the devices deployed in the environment. Additionally, the fog computing paradigm extends the cloud computing paradigm to be closer to the things that produce and act on the intelligent environment, allowing to deal with issues like mobility, real time, low latency, geo-localization, among other aspects. In this sense, in this article we present a middleware, which not only is capable of allowing MAS and SOA to communicate in a bidirectional and transparent way, but also it uses the fog computing paradigm autonomously, according to the context and to the system load factor. Additionally, we analyze the performance of the incorporation of the fog computing paradigm in our middleware and compare it with other works.     

基于政策的层次系统协作模型

为了解决大型层次系统中多Agent间的协作问题,提出了一种基于政策的协作模型。首先分析层次系统中Agent间协作的特点,然后给出相关概念定义,构建Agent政策框架及政策分享机制,最后提出协作模型算法。实践表明,该模型能够在大型、分布式环境中实现资源和服务的协同,使Agent的行为决策更加规范可控,因此具有高度的灵活性及扩展性,对于解决层次MAS协作问题具有一定的参考价值。     

多Agent系统中的角色继承

继承用来描述一般与特殊关系,多Agent系统中引入继承有助于分析多Agent系统的结构层次和软件重用。本文讨论了开发多Agent系统时分析和描述继承的重要性和多Agent系统中继承的特点,通过一个角色模型对继承机制进行了语义定义,提出了一种在需求分析阶段对继承进行建模和分析的方法。     

AGENT COORDINATION CONTEXTS IN A MAS COORDINATION INFRASTRUCTURE

In order to harness complexity in multi-agent systems (MAS), first-class entities that mediate interaction between agents and environment are required, which can encapsulate control over MAS behavior and evolution. To this end, MAS infrastructures should provide mediating artifacts, both enabling and constraining agent interactions, and possibly representing admissible agent perceptions and actions over the environment.

Along this line, in this paper, we take the notion of agent coordination context (ACC) as a means to model agent-environment interactions, and show how it can be embedded within a MAS infrastructure in terms of model and runtime structures. Then, we take the TuCSoN coordination infrastructure as a reference, and extend it with the ACC abstraction to integrate the support for coordination with organization and security.     

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.     

Software selection: a case study of the application of the analytical hierarchical process to the selection of a multimedia authoring system

This paper discusses the multimedia processing environment, the applicability of analytic hierarchy process (AHP) in problem solving, and how AHP can be applied to the selection of multimedia authorizing systems (MAS) in a group decision environment. A MAS selection model is proposed to facilitate the group's decision making in the selection of MAS. Six software engineers, who are technically competent and experienced, participated in our study. They were trained to use AHP and then applied this technique to evaluate three MAS products for adoption decision. The results indicated that AHP offers chances for every participant to fully understand, discuss, and objectively evaluate all MAS products before identifying and selecting the most efficient MAS.     

多智能体系统支撑环境MAS/TH-5

ＭＡＳ／ＴＨ－５系统是具有一定开放性的支持ＭＡＳ构造与集成的支撑环境,它将开发与运行环境集为一体,形成整体解决方案．同时,文中所构造的具有实际应用价值的Ａｇｅｎｔ模型,为支撑环境提供了理论和技术基础．这种模型具有理性平衡的特点．该模型还为用户提供了建立其他Ａｇｅｎｔ模型的一般性框架,可以满足ＭＡＳ系统中Ａｇｅｎｔ交互的需求．在支撑环境上,文中所设计的Ａｇｅｎｔ语言具有描述ＢＤＩ等思维状态的能力,可处理较复杂的实际应用问题．     

An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games

In this paper, we investigate Reinforcement learning (RL) in multi-agent systems (MAS) from an evolutionary dynamical perspective. Typical for a MAS is that the environment is not stationary and the Markov property is not valid. This requires agents to be adaptive. RL is a natural approach to model the learning of individual agents. These Learning algorithms are however known to be sensitive to the correct choice of parameter settings for single agent systems. This issue is more prevalent in the MAS case due to the changing interactions amongst the agents. It is largely an open question for a developer of MAS of how to design the individual agents such that, through learning, the agents as a collective arrive at good solutions. We will show that modeling RL in MAS, by taking an evolutionary game theoretic point of view, is a new and potentially successful way to guide learning agents to the most suitable solution for their task at hand. We show how evolutionary dynamics (ED) from Evolutionary Game Theory can help the developer of a MAS in good choices of parameter settings of the used RL algorithms. The ED essentially predict the equilibriums outcomes of the MAS where the agents use individual RL algorithms. More specifically, we show how the ED predict the learning trajectories of Q-Learners for iterated games. Moreover, we apply our results to (an extension of) the COllective INtelligence framework (COIN). COIN is a proved engineering approach for learning of cooperative tasks in MASs. The utilities of the agents are re-engineered to contribute to the global utility. We show how the improved results for MAS RL in COIN, and a developed extension, are predicted by the ED. Author funded by a doctoral grant of the institute for advancement of scientific technological research in Flanders (IWT).