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。     

自主机器人自组织结构IRASO的仿真研究

自主机器人车辆具有智能性和快速反应的特点,而在精确推理和实时性之间录示最佳折衷是体系结构的关键文中提出基于分布式多Ａｇｅｎｔ系统的自组织体系结构ＩＲＡＳＯ系统的自组织体系结构ＩＲＡＳＯ,Ａｇｅｎｔ之间动态组合以适应环境变化,公告板系统评估环境势态和指导Ａｇｅｎｔ组织,同时设计了Ａｇｅｎｔ协调工作的空间和时间模型,基于ＴＣＰ／ＩＰ的计算联勇域为异质分布式多Ａｇｅｎｔ的协作运行提供支持,仿真结果表明该     

虚拟环境的系统设计方法及计算模型研究

面向对象技术和面向Ａｇｅｎｔ技术是虚环境系统的基本设计方法。本文用面向对象的方法来构造Ａｇｅｎｔ,并提供一组支撑Ａｇｅｎｔ的底层计算模型,如神经网络,遗传算法,专家系统和规划管理等。     

Agent规范与编程语言SPLAW的语法和语义

本文描述了Ａｇｅｎｔ规范与编程语言ＳＰＬＡＷ的语法，为ＳＰＬＡＷ中的各种语言成分提供了操作语义，并基于标记转换系统给出Ａｇｅｎｔ性质的证明规则。由于ＳＰＬＡＷ的通信部分采用不依赖于具体应用的Ａｇｅｎｔ交互语言ＫＱＭＬ，使遵循同一标准的Ａｇｅｎｔ之间能进行协作问题求解。ＳＰＬＡＷ首次为Ａｇｅｎｔ提供继承支持，使其可以实现具有面向对象特征的多线程系统。ＳＰＬＡＷ屏了为语言引入内部模态词的缺陷，试图从外     

Agent计算的理论框架

如何开发复杂、庞大的Ｍｕｌｔｉ－ａｇｅｎｔ系统是目前人们关心的一个重要研究课题．文中提出了Ｍｕｌｔｉ－ａｇｅｎｔ系统中Ａｇｅｎｔ计算的理论框架,以提供方法和概念体系用于刻画和描述Ｍｕｌｔｉ－ａｇｅｎｔ系统的需求、指导系统设计．文中提出了Ａｇｅｎｔ的一种新的体系结构——ＢＩＣＣ体系结构,即Ａｇｅｎｔ是由信念（ｂｅｌｉｅｆ）、意愿（ｉｎｔｅｎｔｉｏｎ）、能力（ｃａｐａｂｉｌｉｔｙ）、承诺（ｃｏｍ ｍ ｉｔｍ ｅｎｔ）构成的意向系统．基于Ｍｕｌｔｉ－ａｇｅｎｔ系统计算的逻辑框架,文中给出了构成Ａｇｅｎｔ体系结构各个认知成份的语义定义,分析了Ａｇｅｎｔ是如何基于该体系结构来进行计算的,最后研究和验证了在动态、不确定的Ｍｕｌｔｉ－ａｇｅｎｔ系统中Ａｇｅｎｔ计算的活性和安全性问题     

基于Markov对策的多Agent强化学习模型及算法研究

在ＭＤＰ,单Ａｇｅｎｔ可以通过强化学习来寻找问题的最优解。但在多Ａｇｅｎｔ系统中,ＭＤＰ模型不再适用。同样极小极大Ｑ算法只能解决采用零和对策模型的ＭＡＳ学习问题。文中采用非零和Ｍａｒｋｏｖ对策作为多Ａｇｅｎｔ系统学习框架,并提出元对策强化学习的学习模型和元对策Ｑ算法。理论证明元对策Ｑ算法收敛在非零和Ｍａｒｋｏｖ对策的元对策最优解。     

基于Agent的工作流模型与应用

ＣＩＭＳ应用集成平台是企业实施ＣＩＭＳ有效支持工具,而ＭＩＳ系统则是整个ＣＩＭＳ集成平台的重要组成部分,主要研究了基于Ａｇｅｎｔ的工作流模型及其在ＭＩＳ系统中的应用,在ＷＦＭＳ参考模型的基础上,提出了一个基于Ａｇｎｅｔ的,易于扩展和移植的,以浏览器作为用户界面的工作流管理系统;ＡＢｓ－ＷＦＭＳ,并介绍了系统的运作方式。基于Ａｇｅｎｔ的工作流管理系统在浙江好来西ＣＩＭＳ系统的开发中作了有益的浓试。     

基于MAPE系统的CSCW框架与协同管理Agent的模型设计

该文针对多Ａｇｅｎｔ处理环境,在［１］［２］基础上给出了一个ＣＳＣＷ的系统框架。为了提高协同工作的效率,在［１０］的基础上提出了协同管理Ａｇｅｎｔ模型。该模型除具有一般Ａｇｅｎｔ的功能外,它还有三个子系统：通讯管理子系统、信息管理子系统和群决策支持系统。并把该框架与模型运用于制造工程领域,提出了一个ＣＡＤ／ＣＡＭ／ＣＡＰＰ一体化的协同工作框架。     

基于主控流动的多级Multi-Agent系统

从分析人的思维活动着手,提出了模拟人类智能活动过程构筑Ｍｕｌｔｉ－Ａｇｅｎｔ系统的方法,并由该方法构造了基于主控流动的多级Ｍｕｌｔｉ－Ａｇｅｎｔ系统。其主要特点是把小 Ｍｕｌｔｉ－Ａｇｅｎｔ系统视为一个特殊的Ａｇｅｎｔ,并由这种特殊的Ａｇｅｎｔ组成更高一级的Ａｇｅｎｔ系统…从而形成多级Ｍｕｌｔｉ－Ａｇｅｎｔ系统,而且在每一级Ａｇｅｎｔ系统中均采用了主控流动策略,使得整个系统能够把巨量的Ａｇｅｎｔ有效地结合起来。系统效率高,能主动改变和适应环境,为设计具体的大规模Ｍｕｌｔｉ－Ａｇｅｎｔ系统提供了一种可行的解决方案。     

多Agent系统在网上协同教学中的应用

本文首先介绍了Ａｇｅｎｔ和多Ａｇｅｎｔ系统（ＭＡＳ）的概念、分类与结构,然后重点分析了一个在网络环境中利用多 Ａｇｅｎｔ技术的协同教学系统,介绍了其基本组成和工作流程。     

基于系统体系结构的多agent协作

多agent系统提供了一种解决复杂问题的分而治之的方法，这就使得agent间的相互协作成为多agent系统研究的一项重要内容，而系统的体系结构直接影响到agent之间的协作。文中主要讨论了基于体系结构的多agent协作，介绍了几种常见的多agent系统体系结构，并提出了一种基于联邦结构的agent协作。     

A systematic approach to evaluating domain-specific modeling language environments for multi-agent systems

Multi-agent systems (MASs) include multiple interacting agents within an environment to provide a solution for complex systems that cannot be easily solved with individual agents or monolithic systems. However, the development of MASs is not trivial due to the various agent properties such as autonomy, responsiveness, and proactiveness, and the need for realization of the many different agent interactions. To support the development of MASs various domain-specific modeling languages (DSMLs) have been introduced that provide a declarative approach for modeling and supporting the generation of agent-based systems. To be effective, the proposed DSMLs need to meet the various stakeholder concerns and the related quality criteria for the corresponding MASs. Unfortunately, very often the evaluation of the DSML is completely missing or has been carried out in idiosyncratic approach. If the DSMLs are not well defined, then implicitly this will have an impact on the quality of the MASs. In this paper, we present an evaluation framework and systematic approach for assessing existing or newly defined DSMLs for MASs. The evaluation is specific for MAS DSMLs and targets both the language and the corresponding tools. To illustrate the evaluation approach, we first present SEA_ML, which is a model-driven MAS DSML for supporting the modeling and generation of agent-based systems. The evaluation of SEA_ML is based on a multi-case study research approach and provides both qualitative evaluation and quantitative analysis. We report on the lessons learned considering the adoption of the evaluation approach as well as the SEA_ML for supporting the generation of agent-based systems.     

协同地球科学计算环境的架构设计

以建立区域气候模拟系统为研究原型,探讨利用Peer-to-Peer 技术和移动Agent 技术构建协同地球科学计算环境（GeoCoEn）。基于P2P 和多Agent 集成的多层体系结构,满足了GeoCoEn 对开放性、灵活性、可扩展性、可移植性以及透明性等方面的需求。     

Extending process automation systems with multi-agent techniques

This paper presents a design of a process automation system extended with multi-agent systems (MAS) and experiments with its implementation. According to this design, MAS can be used to extend the functionality of ordinary process automation systems at higher levels of control. Anticipated benefits of this include enhanced reconfigurability, responsiveness and flexibility of the resulting automation system. The design also takes into account particular characteristics of process automation. An agent platform for process automation is presented as a basis for applying MAS. A FIPA-compliant agent platform is extended with process automation specific functionality. The platform utilizes a hierarchical agent organization and a BDI-agent model. Two applications are implemented using the platform. One of these shows how the techniques of distributed planning can be applied in discrete control. The other provides a model for supervisory continuous control using the techniques of distributed search. Experiments performed with a laboratory test environment using the applications are presented. They are able to demonstrate the feasibility of the approach in test scenarios.     

多Agent系统的组织结构与协同

协同是多Agent系统研究的主要内容,组织结构为Agent成员提供一个相互之间交互的框架以及多Agent群体求解问题的高层观点和相关信息,文章分析了多Agent系统协同所涉及的问题,并对不同组织结构的多Agent协同问题进行了讨论.     

基于学习者个性因素的多Agent协同学习系统

未解决当前的远程教育系统存在形式单一和被动教学等问题,该文提出了一个基于学习者个性因素的多Agent学习系统模型。该模型结合智能代理技术,通过分析学习者个性因素,给出了个体Agent能力描述语言,提出了新的个性化分组策略和学习任务分配策略,采用补偿机制鼓励agent合作,结合状态空间搜索理论使M AS系统具有更强的解题能力,并可满足学习者主动学习的要求,还能在一定程度上节约系统的通讯。     

A MAS IMPLEMENTATION FOR SYSTEM IDENTIFICATION AND PROCESS CONTROL

In this paper, a MAS for system identification and process control is presented. In particular, this MAS implements a self‐tuning regulator (STR) scheme. It has adopted FIPA specifications because they have become a stronger standard in MAS development and they involve not only agent language specifications but also agent management and conversations. In this work, an Ontology Agent (OA) is included, using DAML + OIL as ontology language. The obtained results validate this approach in the implementation of well‐known algorithms for control process.     

Multiscale MAS modelling to simulate the soil environment: Application to soil ecology

Soils are important components of ecosystem and their functioning is of great importance for human well-being. Describing, understanding, foreseeing, and controlling biological activities and functions in soil in the context of global change are major objectives for scientists. Modelling soil bioturbation by soil engineers is of great importance although it is faced with the difficulty to describe the soil physical environment. Creating a model of a soil leads to complexity problems: a soil is a multi-scale heterogeneous, three-dimensional and dynamic environment that cannot be modelled and simulated without defining a suitable and optimized representation and taking assumptions about the studied system. An approach based on fractal theory (often used in soil sciences) was chosen to model such a real complex environment; it was integrated into a multi-agent system (MAS). MAS allows to simulate situated agents (earthworms) in an virtual world. The originality of this present MAS is that it is based on a dynamic environment which builds itself, on demand, according to an abstract canvas tree and agent movements. The aim of this paper is to present this approach and its originality, and to describe the model and the simulator. A theoretical view of the approach is given and applied to a case study: the impact of earthworms on soil structure and organic matter dynamics.     

基于HLA的智能仿真支撑环境研究

引入了分布式人工智能（DAI）中多Agent的概念，将HLA仿真技术框架和多Agent技术相结合，提出了一种具有智能特性的仿真支撑环境，实现了一个通用的Agent仿真对象模型，介绍了如何利用Agent通信语言KQML进行互操作的方法，描述了仿真系统的工作过程。