多智能体技术发展及其应用综述

首先阐述了智能体技术的相关定义及特性,通过分析国内外多智能体技术的应用研究文献,对多智能体系统的基础研究进行分析并梳理了多智能体一致性及控制等方向的技术发展。接着选取了机器人控制和无线传感器网络两个领域重点,探讨了近年来多智能体技术在实际工程中的应用变化与最新成果。最后,总结了多智能体技术在工程应用中有待解决的主要问题,指出了未来多智能体技术应用的研究方向。     

多智能体在城市交通系统中应用现状综述

多智能体技术通过各自治智能体间的通讯、协调、合作来表达实际的复杂系统,特别适用于城市交通系统的建模。在分析了多智能体技术和城市交通系统特性的基础上,对多智能体在城市交通管理系统、出行信息系统和公交系统中的应用现状进行了综合论述,对各个领域最具代表性的应用进行了介绍和分析。研究认为,多智能体技术为城市交通问题提供了良好的解决方案,在具体应用中需要对交通环境和实体进行准确的建模,缓解通信需求、降低运算量及低协调复杂度、增强应用系统的稳定性和安全性。最后指出了多智能体技术在城市交通系统应用中的发展趋势。     

基于多智能体技术的智能CAD系统设计集成化研究

针对ＣＡＤ设计过程集成中存在的技术难题,将多智能体技术应用于智能ＣＡＤ系统的集成化设计环境中,实现多个机械设计专家系统协调的有机集成,提出了采用多智能体理论论建立分布式协调设计体系来研究解决这一例题的新思路,建立一个实际的基于多智能体的智能集成设计系统,并建立了基于一主软件平台验证所提出理论和方法的正确笥和可行性。     

面向智能体的信息系统开发方法研究

智能体技术是近几年来发展起来的一种新的软件技术方法,将多Agent技术引入信息系统(Information System,IS)能使信息系统在新的管理模式中承担感知、协调、决策和指导的作用。文中介绍了智能体概况以及多智能体的几个关键性定义及发展状况,从特性角度、信息角度分析,信息系统适合用Agent技术来描述,从而着重介绍了智能体在两种信息系统中应用,通过分析可以看出多智能体引入信息系统是一种更为有效也是最有发展前途的一种方法。最后构想了未来面向智能体的信息系统开发方法。     

移动智能体安全通信机制研究

移动智能体除具备智能体,多智能体系统已有的技术特点外,它可以按照任务执行的需要,移动到网络节点上去工作.其主要技术特征是实现计算执行程序的异地执行,必须解决移动智能体的安全通信问题.设计了IBMAS,对移动Agent计算进行了系统支持.它提供移动机制、通信机制和安全机制,从而便于用户在此基础上进行应用程序的开发和实际应用.     

多智能体系统安全性问题及防御机制综述

多智能体系统作为分布式人工智能的重要分支,已成为解决大型、复杂、分布式及难预测问题的重要手段。在开放网络中,多智能体系统仍面临许多安全问题,潜在的安全威胁很可能影响其实际应用的稳定性、快速性和准确性。基于目前已知的多智能体系统通用模型,介绍了多智能体系统通信协议、访问控制和协调机制中潜在的安全问题,规范了多智能体系统安全性问题的研究体系,总结了系统设计过程中可行的防御技术和隐私保护技术,最后展望了多智能体系统安全研究的发展方向。     

面向智能体的信息系统开发方法研究

智能体技术是近几年来发展起来的一种新的软件技术方法，将多Agent技术引入信息系统（Information System，IS）能使信息系统在新的管理模式中承担感知、协调、决策和指导的作用。文中介绍了智能体概况以及多智能体的几个关键性定义及发展状况，从特性角度、信息角度分析，信息系统适合用Agent技术来描述，从而着重介绍了智能体在两种信息系统中应用，通过分析可以看出多智能体引入信息系统是一种更为有效也是最有发展前途的一种方法。最后构想了未来面向智能体的信息系统开发方法。     

群智能在多智能体系统中的应用研究进展

群智能算法是受群居性昆虫群体的集体行为启发而设计的分布式问题求解方法,将它应用到多智能体系统,旨在提高系统的鲁棒性、灵活性和自适应性。以群智能在多智能体系统中的应用为线索,首先介绍群智能的核心机制,然后从多智能体系统通信机制、协作技术、学习问题及体系结构建立这几个方面总结群智能理论在多智能体系统中的已有工作。最后分析和讨论了群智能方法在多智能体系统应用中存在的问题,并提出今后的工作展望。     

基于多智能体的组合列车同步制动系统在线诊断

针对组合列车的同步制动系统结构复杂多变、分布控制的特点,设计了一种基于多智能体技术的在线诊断系统,将复杂的诊断任务逐级分解为一系列相对简单的子任务,由多个智能体协作完成诊断。给出了双层结构的多智能体诊断体系,并对其中各个组成部分的功能和设计方法进行了简单介绍。在完成诊断系统总体设计的基础上,重点研究了诊断任务的分解、分配及智能体间的协作策略等关键问题。实际应用结果证明了该技术的有效性,对保障组合列车安全运行有正面意义。     

基于多智能体的产品报价研究

产品报价涉及企业生产、技术、销售、财务、管理等部门，影响因素包括技术水平、设备状况、市场环境、竞争对手、原材料、物流成本等，过程非常复杂。如何有效地估计相关的生产成本是此文的主要目的。文中探讨多智能体技术在产品报价中的应用，利用多智能体的判断、推理能力和与其他智能体通讯协作能力来解决产品报价中影响因素多、效率低和准确性差的问题。提出了基于多智能体的产品报价系统的构造方法，建立了多智能体产品报价模型。案例证明，该模型能够提高报价的效率和准确性。     

Multiagent systems and information retrieval our experience with X.MAS

Autonomous agents and multiagent systems have been successfully applied to a number of problems and have been largely used in different application fields. In particular, in this paper we are interested in information retrieval. In fact, in this field multiagent solutions are very useful and effective since they decouple the problem in a network of software agents that interact to solve problems that are beyond the individual capabilities or knowledge. In so doing, multiagent systems allow to overwhelm typical problems of single agent and centralized approaches. To discuss the lesson learnt in using the multiagent technology in the field of information retrieval, in this paper, we present our experience in using X.MAS, a generic multiagent architecture aimed at retrieving, filtering and reorganizing information according to user interests. To this end, after presenting X.MAS, we illustrate six applications built upon it. Our conclusion is that multiagent technology is quite effective to design and realize concrete information retrieval applications.     

A modern approach to multiagent development

Multiagent systems (MAS) development frameworks aim at facilitating the development and administration of agent-based applications. Currently relevant tools, such as JADE, offer huge possibilities but they are generally linked to a specific technology (commonly Java). This fact may limit some application domains when deploying MAS, such as low efficiency or programming language restrictions. To contribute to the evolution of multiagent development tools and to overcome these constraints, we introduce a multiagent platform based on the FIPA standards and built on top of a modern object-oriented middleware. Experimental results prove the scalability and the short response-time of the proposal and justify the design and development of modern tools to contribute the multiagent technology.     

Self-organization and multiagent systems: II. Applications and the development technology

Major trends in the development of modern information technologies are to a large extend determined by important practical problems that arise in economics, ecology, safety of society and individuals, and in other fields. Even though these problems seem to be quite different and the requirements for their software implementation are also different, they have many common features, which imply the most stringent requirements for modern information technologies. These features were analyzed in the first part of the present paper. That analysis showed that the new requirements for the model and software implementation of such problems are best met by the multiagent self-organizing system model. In this paper, we consider examples of using this model in various applications and describe their architectures and software implementation; in particular, multiagent self-organization models as applied for flood forecasting and planning and operational enterprise management are described. New capabilities of multiagent self-organizing systems are demonstrated using a self-learning system for detecting intrusions into computer networks as an example. Here, the problem of self-configuration of an overlay network is actually solved. The capabilities of a multiagent self-organizing system in large-scale control in real time are demonstrated using adaptive traffic control in large cities. For the software implementation of multiagent self-organizing systems, special development tools that are different from the existing ones are needed because the conventional top-down development paradigm is inappropriate for self-organizing architectures. The cause is that the global behavior of a multiagent self-organizing system emerges due to local interactions; therefore, it cannot be predicted in advance. For that reason, the bottom-up development model is more appropriate for such systems. In this paper, we give a brief review of the models and approaches proposed for this purpose. One of the promising approaches based on the use of the so-called self-organization design patterns is described in more detail. Results of using the multiagent self-organization model are discussed and prospects of its practical application are estimated.     

Infrastructures for the environment of multiagent systems

The notion of environment is receiving an increasing attention in the development of multiagent applications. This is witnessed by the emergence of a number of infrastructures providing agent designers with useful means to develop the agent environment, and thus to structure an effective multiagent application. In this paper we analyse the role and features of such infrastructures, and survey some relevant examples. We endorse a general viewpoint where the environment of a multiagent system is seen as a set of basic bricks we call environment abstractions, which (i) provide agents with services useful for achieving individual and social goals, and (ii) are supported by some underlying software infrastructure managing their creation and exploitation. Accordingly, we focus the survey on the opportunities that environment infrastructures provide to system designers when developing multiagent applications.     

多智能体协作技术综述

本文从协作模型和研究方法两个角度考察了多智能体协作技术的发展,认为是外在的 理性形式化模型和内在的对策和学习机制是多智能体协作技术的两个重要方面．并对这两个 方面的研究和应用前景作了进一步的展望．     

Foundations of distributed interaction systems

There are numerous applications where a variety of human and software participants interactively pursue a given task (play a game, engage in a simulation, etc.). In this paper, we define a basic architecture for a distributed, interactive system (DIS for short). We then formally define a mathematical construct called a DIS abstraction that provides a theoretical basis for a software platform for building distributed interactive systems. Our framework provides a language for building multiagent applications where each agent has its own behaviors and where the behavior of the multiagent application as a whole is governed by one or more “master” agents. Agents in such a multiagent application may compete for resources, may attempt to take actions based on incorrect beliefs, may attempt to take actions that conflict with actions being concurrently attempted by other agents, and so on. Master agents mediate such conflicts. Our language for building agents (ordinary and master) depends critically on a notion called a “generalized constraint” that we define. All agents attempt to optimize an objective function while satisfying such generalized constraints that the agent is bound to preserve. We develop several algorithms to determine how an agent satisfies its generalized constraints in response to events in the multiagent application. We experimentally evaluate these algorithms in an attempt to understand their advantages and disadvantages. This revised version was published online in June 2006 with corrections to the Cover Date.     

Visualization and debugging of distributed multiagent systems

Visualizing the behavior of systems with distributed data, control, and process is a notoriously difficult task. Each component in the distributed system has only a local view of the whole setup, and the onus is on the user to integrate, into a coherent whole, the large amounts of limited information they provide. In this article, we describe an architecture and an implemented system for visualizing and controlling distributed multiagent applications. The system comprises a suite of tools, with each tool providing a different perspective of the application being visualized . Each tool interrogates the components of the distributed application, collates the returned information, and presents this information to users in an appropriate manner. This in essence, shifts the burden ofinference from the user to the visualizer. Our visualizer has been evaluated on four distributed multiagent systems: a travel management application, a telecommunications network management application, a business process management demonstrator, and an electronic commerce application. Lastly, we briefly show how the suite of tools can be used together for debugging multiagent applications - a process we refer to as debugging via corroboration.     

Environment as a first class abstraction in multiagent systems

The current practice in multiagent systems typically associates the environment with resources that are external to agents and their communication infrastructure. Advanced uses of the environment include infrastructures for indirect coordination, such as digital pheromones, or support for governed interaction in electronic institutions. Yet, in general, the notion of environment is not well defined. Functionalities of the environment are often dealt with implicitly or in an ad hoc manner. This is not only poor engineering practice, it also hinders engineers to exploit the full potential of the environment in multiagent systems. In this paper, we put forward the environment as an explicit part of multiagent systems.We give a definition stating that the environment in a multiagent system is a first-class abstraction with dual roles: (1) the environment provides the surrounding conditions for agents to exist, which implies that the environment is an essential part of every multiagent system, and (2) the environment provides an exploitable design abstraction for building multiagent system applications. We discuss the responsibilities of such an environment in multiagent systems and we present a reference model for the environment that can serve as a basis for environment engineering. To illustrate the power of the environment as a design abstraction, we show how the environment is successfully exploited in a real world application. Considering the environment as a first-class abstraction in multiagent systems opens up new horizons for research and development in multiagent systems.     

Agent Mining: The Synergy of Agents and Data Mining

Autonomous agents and multiagent systems (or agents) and data mining and knowledge discovery (or data mining) are two of the most active areas in information technology. Ongoing research has revealed a number of intrinsic challenges and problems facing each area, which can't be addressed solely within the confines of the respective discipline. A profound insight of bringing these two communities together has unveiled a tremendous potential for new opportunities and wider applications through the synergy of agents and data mining. With increasing interest in this synergy, agent mining is emerging as a new research field studying the interaction and integration of agents and data mining. In this paper, we give an overall perspective of the driving forces, theoretical underpinnings, main research issues, and application domains of this field, while addressing the state-of-the-art of agent mining research and development. Our review is divided into three key research topics: agent-driven data mining, data mining-driven agents, and joint issues in the synergy of agents and data mining. This new and promising field exhibits a great potential for groundbreaking work from foundational, technological and practical perspectives.     

传感器技术在电网中的应用评述

传感器技术作为重要的信息采集技术,在电网中的应用越来越广泛,由于电网应用场景的特殊性,也在影响着传感器技术应用的深度和广度。介绍了传感器技术的国内外发展现状;对传感器技术在电网中的应用现状进行了阐述,并结合实际应用情况,对传感器技术在电网应用中面临的问题进行了评述和分析。最后对传感器技术在电网中的应用进行了总结和展望。