一种基于蚁群算法的多任务联盟串行生成算法

联盟生成是多Agent系统的一个关键问题,主要研究如何在多Agent系统中动态生成面向任务的最优Agent联盟.引入蚁群算法解决多任务联盟问题.提出了一种基于蚁群算法的多任务联盟串行生成算法,对于任务序列可依次生成全局最优联盟,避免了联盟死锁和资源浪费,同时算法基于蚁群系统的学习能力可以有效减少联盟生成的搜索时间和计算量,可实现性好.     

基于多Agent技术的城市交通信号控制系统的研究

简介了多Agent系统技术，对多Agent系统与传统的复杂系统进行了比较。然后根据我国城市混合交通的特点提出了一种新的基于多Agent技术的城市交通信号控制系统的体系结构。该系统采用部分全局的协调方式来协调相邻交叉口Agent之间的关系。     

基于Agent的协同式网络管理模型研究与实现

移动Agent技术为智能网络管理的研究开辟了新的思路,但由于每个Agent是一个自治的主体,因此如何实现多个Agent协调合作来高效地管理网络成为多Agent系统(MAS)的核心问题.在对移动Agent和其体系结构进行研究的基础上,提出了一种基于多移动Agent网络管理模型,将移动Agent分成四类,并将黑板系统方法的应用在多Agent之间通信和协调中,使Agent问的协调变得相对容易,通过黑板将单个Agent联系成一个整体,结果表明,该模型加强了对各Agent的控制,提高了网络管理效率.     

基于多Agent的动态负载平衡算法的应用研究

基于多Agent的动态负载平衡算法的基础为动态负载平衡算法基本原理,是通过对其4种节点机、算法任务调度模型,讨论DMAL算法实现规,及任务分类、迁移粒度和负载迁移触发时机的问题,再引入多Agent技术提高负载平衡时节点间处理的通信能力,进而将可基于DMAL算法的一种负载平衡框架结构提供给分布式处理系统,并对现有负载平衡算法、不作负载平衡运行结果及DMAL算法进行仿真实验,以证明基于多Agent的动态负载平衡算法方案在实际应用中是具有可行性、有效性的。     

基于Agent的网络入侵检测系统

在网络流量不断增大的情况下,网络入侵检测系统(NIDS)会产生丢包问题.针对这个问题,论文设计了一种基于Agent的NIDS.该系统根据网络流量的大小,可以动态的调整系统中检测Agent的数目,以起到分担网络流量同时又不过多消耗系统资源的作用.实验结果表明,在系统中检测Agent的数目大于1的情况下,各检测Agent分担了整个网络的流量,在一定程度上解决了NIDS的丢包问题.     

不确定条件下的多Agent自动协商模型

考察了在不确定条件下的双边多议题协商中对Agent的效用建模的问题,给出一个引入了不确定因素的Agent的效用函数形式,更为准确地描述了不确定条件下Agent的偏好.提出了一个不确定条件下的自动协商模型,基于遗传算法设计了协商算法.仿真实验结果表明,该模型能够降低风险,改善Agent在协商过程中的决策效果.     

基于JACK平台的多Agent系统设计方法研究

随着信息技术的飞速发展,应用软件变得越来越复杂,其智能化程度也越来越高。面向Agent软件开发技术就是在此背景下应运而生的,并迅速成为软件工程领域的一个研究热点JACK平台是以BDI模型为基础,基于JAVA语言的多Agent系统开发环境。此外,它还支持Prometheus等主流面向Agent软件开发方法。通过一个具体项目案例“图书智能推荐系统”给出TJACK平台下多Agent系统的设计与实现方法。     

一种新型的网格资源监控模型的研究与设计

为了实时准确监控网格中的资源情况,本文研究并引入移动Agent技术,建立了基于移动Agent技术的网格资源监控模型MAGM(Mobile Agent Grid Monitor),提出了解决LBA组件内各类Agent负载均衡协作问题的算法.在基于Globus和IBM的Aglet的网格实验平台上进行的仿真实验,结果表明MAGM模型的有效性.     

基于Agent的动态负载均衡技术及仿真实现

实现负载均衡需要解决三个关键问题，即负载状况的定义、负载探测以及均衡策略。文章分析了现有负载均衡技术存在的问题．这些问题包括负载探测的间接性，负载均衡的单向性、静态性和主观性等；提出一种基于Multi—Agent的动态负载探测技术和均衡策略，给出了相应的结构模型和算法，并进行了仿真试验，结果表明该技术优于其它负载均衡技术。     

基于离散粒子群算法求解复杂联盟生成问题

针对联盟生成问题现有解决方案的不足,提出复杂联盟和虚拟Agent的概念,设计一种基于多粒子群协同优化的复杂联盟串行生成算法,实现一个Agent可以加入多个联盟和一个联盟可以承担多个任务,在一定程度上解决了Agent资源和能力的浪费问题.实验结果证明了算法的有效性.     

A Study of Applications of Multiagent System Specifications and the Key Techniques in Automatic Abstracts System

A Study of Applications of Multiagent System Specifications and the Key Techniques in Automatic Abstracts System     

Adaptive agents and multiagent systems

New information systems and recent applications (grid computing, Web Services, and so on) are often distributed, large-scale, open, heterogeneous, and characterized by a dynamic environment. To model these complex systems, researchers have spent much effort during the last few years on multiagent systems. The aim is to model complex distributed systems as a set of (possibly organized) software agents that interact in a common environment. The decomposition of a system into a number of agents lets the system react and adapt better in a changing environment. Moreover, organized structures ("social" structures) can emerge from interactions between agents, which in turn constrain and coordinate the agents' behavior. A multiagent system takes its metaphors of interaction from social systems rather than using the metaphor of the isolated thinker that early artificial intelligence researchers preferred. An important issue when dealing with this increasing complexity is to build adaptive agents and multiagent systems. Agents and multiagent systems must be aware of their own capabilities and of changes to other agents and their environment. To remain effective, agents must be able to adapt their structures and knowledge while they execute.     

Multiagent Coordination Techniques for Complex Environments: The Case of a Fleet of Combat Ships

The use of agent and multiagent techniques to assist humans in their daily routines has been increasing for many years, notably in command and control C2 systems. In this context, we propose using multiagent planning and coordination techniques for resources management in C2 systems. The particular problem we studied is the design of a decision-support for antiair warfare on combat ships. In this paper, we refer to the specific case of several combat ships defending against incoming threats and where coordination of their respective resources is a complex problem of capital importance. Efficient coordination mechanisms between the different combat ships are then important to avoid redundancy in engagements and inefficient defence caused by the conflicting actions. To this end, we present four different coordination mechanisms based on task sharing. Three of these mechanisms are communication-based: central coordination, contract Net coordination, and ~ Brown coordination, while the last one is a zone defence coordination and is based on conventions. Finally, we present the results obtained while simulating these various mechanisms     

Ein Multi-Agentensystem für die modellbasierte Echtzeit-Fehlerdiagnose

Real-time AI systems, among them multiagent systems, are gaining importance in complex technical applications. This paper presents VEX, an ambitious multiagent system for model-based real-time fault diagnosis in modular production systems. The architecture comprises several process agents, a simulation agent and a user agent. We discuss the most interesting characteristics of this real-time AI architecture. As implementation examples we present the multi-DSP core of the simulation agent, the client-server communication protocol for the process agents and the user agent’s graphical interface.     

A Comprehensive Survey of Multiagent Reinforcement Learning

Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must, instead, discover a solution on their own, using learning. A significant part of the research on multiagent learning concerns reinforcement learning techniques. This paper provides a comprehensive survey of multiagent reinforcement learning (MARL). A central issue in the field is the formal statement of the multiagent learning goal. Different viewpoints on this issue have led to the proposal of many different goals, among which two focal points can be distinguished: stability of the agents' learning dynamics, and adaptation to the changing behavior of the other agents. The MARL algorithms described in the literature aim---either explicitly or implicitly---at one of these two goals or at a combination of both, in a fully cooperative, fully competitive, or more general setting. A representative selection of these algorithms is discussed in detail in this paper, together with the specific issues that arise in each category. Additionally, the benefits and challenges of MARL are described along with some of the problem domains where the MARL techniques have been applied. Finally, an outlook for the field is provided.     

Planning agents in JAMES

Testing is an obligatory step in developing multiagent systems. For testing multiagent systems in virtual, dynamic environments, simulation systems are required that support a modular, declarative construction of experimental frames, that facilitate the embedding of a variety of agent architectures and that allow an efficient parallel, distributed execution. We introduce the system JAMES (a Java based agent modeling environment for simulation). In JAMES, agents and their dynamic environment are modeled as reflective, time-triggered state automata. Its possibilities to compose experimental frames based on predefined components, to express temporal interdependencies, to capture the phenomenon of proactiveness and reflectivity of agents are illuminated by experiments with planning agents. The underlying planning system is a general-purpose system, about which no empirical results exist besides traditional static benchmark tests. We analyze the interplay between heuristics for selecting goals, viewing range, commitment strategies, explorativeness, and trust in the persistence of the world and uncover properties of the the agent, the planning engine, and the chosen test scenario: TILEWORLD     

车路通信环境下TD-LTE无线资源调度建模与仿真

为了改善现有车路通信方式覆盖范围小、交付时延大和传输速率低的现状,将TD-LTE技术引入车路通信接入系统。提出了基于TD-LTE的车路通信接入系统无线资源调度模型;构建了车路通信仿真场景,对比例公平算法、指数比例公平算法和改进的最大权重时延优先算法进行性能分析。仿真结果表明:车辆低速移动场景下,系统负载较低时指数比例公平算法性能较好,系统负载较高时改进的最大权重时延优先算法表现更优,比例公平算法不适合车路通信多媒体业务流调度;车辆高速移动场景下,三者都不适合车路通信多媒体业务流调度。     

Emerging Clapping Synchronization From a Complex Multiagent Network With Local Information via Local Control

Recently, complex multiagent networks have received increasing attention from various fields of science and engineering. Synchronization is a typical collective behavior of complex multiagent networks that has extensively been investigated over the last decade. To reveal the dynamical mechanism of synchronization in complex multiagent networks, a simple complex multiagent network with local information is then further investigated. Based on a suitable model, we analyze the inherent key factors in the emerging clapping synchronization. In particular, we explore two challenging fundamental questions: 1) How does the number of informed agents (or backbones) affect the emerging clapping synchronization? 2) How does the distribution of informed agents (or backbones) affect the emerging clapping synchronization? Our results indicate that the emerging clapping synchronization has a great diversity of routes, uncertainty, and adaptability. Moreover, our model and approach provide a possible route for analyzing the other collective behaviors of complex multiagent networks with local information via local control.     

Tracking and Coordination of Multiple Agents Using Sensor Networks: System Design, Algorithms and Experiments

This paper considers the problem of pursuit evasion games (PEGs), where the objective of a group of pursuers is to chase and capture a group of evaders in minimum time with the aid of a sensor network. The main challenge in developing a real-time control system using sensor networks is the inconsistency in sensor measurements due to packet loss, communication delay, and false detections. We address this challenge by developing a real-time hierarchical control system, named LochNess, which decouples the estimation of evader states from the control of pursuers via multiple layers of data fusion. The multiple layers of data fusion convert noisy, inconsistent, and bursty sensor measurements into a consistent set of fused measurements. Three novel algorithms are developed for LochNess: multisensor fusion, hierarchical multitarget tracking, and multiagent coordination algorithms. The multisensor fusion algorithm converts correlated sensor measurements into position estimates, the hierarchical multitarget tracking algorithm based on Markov chain Monte Carlo data association (MCMCDA) tracks an unknown number of targets, and the multiagent coordination algorithm coordinates pursuers to chase and capture evaders using robust minimum-time control. The control system LochNess is evaluated in simulation and successfully demonstrated using a large-scale outdoor sensor network deployment