基于Agent的智能决策支持系统开发与应用

针对传统决策支持系统的局限性,该文提出了一个基于Agent的智能决策支持系统开发方案。利用界面Agent、信息Agent、移动Agent、协作Agent来实现系统的智能部件,多Agent通过协作Agent信息路由器进行协作和交互,共同完成决策支持任务。并将该模型应用于草业信息管理决策支持系统中,系统运行结果证明了模型的有效性和科学性。     

基于Agent的航空安全管理智能决策支持系统

基于基元事件分析法，给出了一个Internet上用多Agent实现的航空公司安全管理智能决策支持系统AAIDSS的设计与实现方法，该系统在决策支持系统3库结构的基础上，采用了多智能体技术。提出了由OLTP处理、基础结构部分、Agent组织共同组建智能决策支持系统的框架模型，给出了事故分析Agent、界面Agent的核心技术。AAIDSS的设计和实现方法，对于构建基于Intranet的中小型智能决策支持系统具有普遍的参考价值。     

基于Multi Agent的分布式项目管理决策支持系统研究*

详细分析了Agent、MultiAgent系统、分布式决策支持系统以及分布式项目管理的特点,将MultiAgent技术应用于分布式项目管理决策支持系统(DPMDSS)的构建,引入了界面Agent、实例化Agent、任务Agent和协调Agent。该系统利用多个Agent的通信与合作,能够更好地辅助项目管理人员进行决策。最后指出该系统在设计过程中需要注意的几个关键问题。     

一个基于JaVa的多Agent系统框架JAFMAS及其改进

介绍一种基于Java多Agent系统构造框架JAFMAS。该框架可为多Agent系统的开发者提供了一组可扩展的Java类,使得开发者将力量集中于Agent的具体功能细节而不必担心Agent的通信方法和行为协调机制。该文介绍了框架的体系结构和分布式环境下Agent间的通信方法和行为协调机制,同时对JAFMAS做了一些改进。     

基于多Agent的分布式智能群决策支持系统研究

在基于B／S和J2EE的结构下,对分布式智能群决策支持系统（DIGDSS）的框架和实现技术进行研究,提出了基于UML的多Agent建模方法和实施步骤,设计了基于多Agent DIGDSS的总体框架,并在此基础上提出了基于B／S和J2EE的DIGDSS软件结构与实现过程。     

多代理系统——Bond的分析

Bond系统是由美国Purdue大学研制的一个分布式多Agent的系统。该系统是基于Java的一个Agent框架,实现了通过中间件的消息传递,目录服务,监控服务和安全等方面的服务,允许用户方便地创建多个Agent,在分布式领域有广泛的应用潜力。本文在分析代码的基础上,对Bond系统做了一个较全面的分析。     

多代理系统--Bond的分析

Bond系统是由美国Purdue大学研制的一个分布式多Agent的系统.该系统是基于Java的一个Agent框架,实现了通过中间件的消息传递,目录服务,监控服务和安全等方面的服务,允许用户方便地创建多个Agent,在分布式领域有广泛的应用潜力.本文在分析代码的基础上,对Bond系统做了一个较全面的分析.     

基于Multi-Agent的分布式智能视觉监控系统研究

90年代以来,随着计算机技术的发展,对于Agent的研究已经成为分布式人工智能领域的一个热点,基于Multi-Agent的分布式智能系统已成功地应用于众多领域。该文将Multi-Agent技术应用于智能视觉监控领域,以开发智能视觉监控的分布式Multi-Agent系统,提出了系统的基本框架和系统实现途径及方法,其中着重研究了基于Multi-Agent理论的视觉监控系统模型、组织活动,以及多Agent之间的协调合作问题。该分布式系统的研究,为实际应用系统的研制开发提供了理论指导和方法依据。     

智能Agent环境下的组织决策支持系统研究

基于Agent协作交互的组织决策支持系统(ODSS)的研究是智能信息系统研究的热点。该文在阐述了组织决策支持系统和智能Agent基础上,提出了基于Agent协作交互的SG-ODSS系统的结构和组成,包括组织Agent、处理Agent组、通信Agent等,介绍了该系统在某大型电器公司的应用。     

基于Agent图的三级多Agent系统组织模型

多Agent系统的组织结构是Agent个体之间交互的框架。对分布式多Agent系统的组织方式、协作机制进行了简要讨论,提出了Agent域及Agent图的概念。根据不同Agent之间的地理位置和通信代价,由Agent个体、Agent组及Agent域三级组织结构形成一个Agent图,并借鉴计算机网络的分布式自适应路由选择策略进行多Agent系统的协作组织。分析表明,该模型具有高效、健壮、通信开销较小等优点。     

Globus支持下基于网格的开放式决策支持系统设计与实现

网格技术的出现和成熟将给决策支持系统(DSS)带来一场新的变革．提出了一种新型的DSS——基于网格的开放式决策支持系统(GBODSS)，它充分地利用了网格技术的优势，将网格技术和DSS结合在一起，并给出了该框架利用Globus工具的一种实现方案．     

The development of decision support systems in China

The short history of development of Decision Support Systems (DSS) in China is described. Some examples of DSS are illustrated. And four main directions involved in the DSS development are pointed out.     

An investigation on the nature of decision support system software

Decision Support Systems (DSS) software is investigated and a conceptual model presented in this paper. The purpose of this model is to facilitate a better understanding of the nature of DSS software. The model is composed of six problem transformational processes linking up seven problem phases. The six transformational processes are: problem transforming system, problem mapping system, problem solving system, procedural program generation system, programming language compiling system, and code generation system, by identifying the seven problem phases this hierarchical provides a conceptual foundation for developing DSS software. Spun-off from this model is a framework for implementing knowledge-based DSS with automatic modeling capabilities. The structure of future DSS software to run on fifth generation computers is also addressed.     

The emerging role of DSS: From passive to active

Existing decision support systems (DSS) are passive in their operation; their sole aim is to help maintain the current position of the firm. Conversely, Information Technology has recently adopted a more active role in the corporate strategy. This suggests that DSS could also undertake a far more active stance by identifying gaps in existing operations and suggesting ways to strengthen the standing of the firm. We renamed DSS containing this characteristics as Active Decision Support Systems (or ADSS) to emphasize their new orientation. Managers draw on their knowledge to suggest ways of alleviating problems or exploiting opportunities, and to evaluate each alternative in relation to the corporate objectives. Patterns and alternatives must be identified and incorporated into the ADSS. They should then be compared with the current position of the firm in order to determine whether any such problems/opportunities exist. This paper proposes an approach for designing ADSS; it introduces a knowledge-based component within the DSS framework and applies the concept of triggers to control the invocation of rule sets. A logical structure for ADSS is provided and the system use is illustrated through an example of strategic management. Issues for future research are highlighted.     

Letter to the Editor

This paper examines how an object called a decision support system (DSS) is a system from the point of view of GST (general systems theory) and how GST is related with the development of DSS in a positive way. Since a DSS is a complicated system, it cannot be described by one model. We will proceed the consideration in the following different levels: functional level, structural level, process level and physical level. We will show that on each level GST provides a useful framework to deal with a DSS and the general formulation by GST directs what is to be done in the development of DSS. On the progress of the development we have found GST can help us with conceptualizing a DSS which is most important in finding a correct research direction. The presented models are the basic framework of our current system, a product of GST.     

A structured modeling based methodology to design decision support systems

Many Decision Support Systems (DSS) support the decision making process through the use of mathematical models and data. DSS design involves modeling data as well as mathematical relationships in a domain. The process of model formulation and subsequent integration of model with data in a DSS is a complex and ill-structured process. This paper proposes a methodology based on Structured Modeling (SM), originally introduced by Geoffrion together with the modeling language SML, to model and design the DSS. The methodology includes rigorous and step by step procedures to design and integrate data and modelbases. The main contribution of our approach lies in the integration of research in database design, and mathematical model formulation within the structured modeling framework. The resultant procedures can be easily automated and taught to students in DSS courses. The motivation for our research stemmed from our constant frustrations in teaching DSS courses over the last five years. In the last two years, when we used our methodology, the performance of the students improved significantly. The average score in the DSS project went up to 85 from 60. Our positive experience in using our methodology in classes over the past two years suggests that the methodology imposes structure into the analysis of decision problems, and as a result students produce better DSS designs for classroom cases.     

多Agent系统安全机制研究

安全问题的处理对于开发多agent系统是很重要的。本文总结了多agent系统概念和特性,重点讨论了其安全方面的问题,包括agent执行安全、agent通信安全等。最后,就agent通信安全技术做了介绍。     

多智能体的增强学习及其在RoboCup中的应用

针对非确定马尔可夫环境下的多智能体系统,提出了多智能体Q学习模型和算法。算法中通过对联合动作的统计来学习其它智能体的行为策略,并利用智能体策略向量的全概率分布保证了对联合最优动作的选择。在实验中,成功实现了智能体的决策,提高了AFU队的整体的对抗能力,证明了算法的有效性和可行性。     

A synopsis of present day practices concerning decision support systems

Selecting a decision support system that fits specific needs, both technically and financially, is a difficult task. The marketplace has been flooded with software bearing the “DSS” label. To provide a clearer picture, the article examines a number of popular DSS definitions and its development. DSS are broken into groups with similar characteristics to examine current software and applications. These groups include Personal Support Systems, DSS and Local Area Networks, DSS and Emulation, Timesharing Systems, Commercial Data Base Systems, and Integrated System. Within each group, current software and practices are reviewed. The potential buyer has the opportunity to compare DSS packages and possible applications. In addition, a list of important questions to ask when choosing DSS Software is included.