协同设计环境下的任务分配方法的研究

介绍一种基于辅助Agent的合同网模型的任务分配方法,并给出了协作过程中的任务选择策略和Agent选择策略,改进基本合同网模型的缺点和不足,以解决多Agent的协同设计系统中分布合作求解问题和任务分配,达到全局最优化,产生高质量的设计及有效的资源利用,并减少协商时间和信息流量,提高系统效率。     

基于多Agent上网管理系统的研究

针对网络监管越来越困难的问题,本文基于Agent设计出网络监控系统,对网络中的共性问题进行监控管理。首先从单个Agent分析其体系结构,描述多Agent技术和特点;其次对网络监控系统的功能和结构进行了详细的分析;最后给出了系统的部分核心代码。     

改进的任务分配策略在WSN中的应用研究

Agent协调是多Agent系统发挥整体效能的保证,也是Agent系统的一个核心问题。合同网模型是多Agent系统中经典的协调策略,但仍有许多不足。主要对合同网中标策略进行改进,提出了一种带有精准度的任务分配策略。用精准度对Agent的投标值进行评价,使Agent的投标值更接近Agent的实际能力。并且对以往多属性任务分配策略中的能力和负载进行了更为详细的评价,对Agent的能力进行了详细的分析后,将任务完成质量的好坏和任务花费代价归为对Agent的能力大小的评价,而对负载的改进则是加入了对任务列表中任务数量的评价。最后以无线传感器网络为应用背景对提出的改进方法进行了仿真实验。     

基于多Agent的汉字签名认证系统的任务分配策略研究*

针对基于多Agent的汉字签名认证系统的任务分配策略进行了讨论,并结合遗传算法和模拟退火算法给出了一种新的基于多Agent的汉字签名认证系统的任务分配策略。     

基于移动Agent分布式计算资源整合与分配

针对分布计算环境下企业资源管理中的诸多问题,提出基于移动Agent的分布计算环境下企业资源的全局整合与动态分配方法和机制,给出了系统的体系结构,以及分布计算环境下移动Agent的规范化模型-BPG模型.提出了基于资源类的资源整合模式和资源类的信息模型,设计了共享资源整合索引结构树,构建了企业集群资源的分级整合.给出了基于移动Agent寻优的资源分配机制,基于市场原则的资源分配调度算法.系统实例表明,基于移动Agent的资源全局整合与分配方法和实现机制是可行的、有效的,对提高网络环境中的资源交互和访问,改善网络访问效率和质量具有一定应用价值.     

基于软件Agent的多Agent数据库中间件系统模型研究

本文首先研究了数据库中间件和多Agent技术，然后从理论上论述了将Agent技术引入数据库中间件系统的可行性，给出了一个基于软件Agent的多Agent数据库中间件系统模型，并洋细讨论该系统模型的方案设计。该系统方案较为有效地解决了网络信息系统中数据交互效率、数据存储与交互的安全性以及系统负载的均衡等方面的问题。     

基于网络的并行计算系统模型

随着Internet迅速发展,利用网上资源构筑分布式并行计算环境进行中、大粒度任务的分布式并行计算已呈现出重要研究价值。另外,Agent理论的日益成熟及多Agent系统MAS(Multi-Agent System)的出现,为开放式分布系统的开发和应用提供了新的模式。结合移动Agent在并行计算中的任务特性,对网络并行计算进行了抽象的分析和描述,然后给出了一个基于移动Agent计算的任务流模型实例。     

基于网络的并行计算系统模型

随着Internet迅速发展,利用网上资源构筑分布式并行计算环境进行中、大粒度任务的分布式并行计算已呈现出重要研究价值。另外,Agent理论的日益成熟及多Agent系统MAS（Multi-Agent System）的出现,为开放式分布系统的开发和应用提供了新的模式。结合移动Agent在并行计算中的任务特性,对网络并行计算进行了抽象的分析和描述,然后给出了一个基于移动Agent计算的任务流模型实例。     

基于MAS的智能交互界面研究及应用

自适应人机交互界面的设计是应用系统能否成功实施与使用的关键,基于此提出了将多Agent系统引入用户界面设计中。分析了交互Agent模型的性质、作用和结构,讨论了几种典型界面模型的特点。实现了一种通过广义状态转移网络学习、进化、预测用户行为并能自主动作的自适应界面Agent模型,并结合应用实例说明了其工作机制。     

基于多Agent的网络取证自适应技术研究

在分析目前网络取证系统的不足和多Agent自适应技术特点的基础上，本文提出并建立了自适应网络取证模型，针对其中的检测与决策取证两个关键Agent工作原理进行了详细探讨，并就其自适应性进行了论述。     

Locality-sensitive task allocation and load balancing in networked multiagent systems: Talent versus centrality

With the development of large scale multiagent systems, agents are always organized in network structures where each agent interacts only with its immediate neighbors in the network. Coordination among networked agents is a critical issue which mainly includes two aspects: task allocation and load balancing; in traditional approach, the resources of agents are crucial to their abilities to get tasks, which is called talent-based allocation. However, in networked multiagent systems, the tasks may spend so much communication costs among agents that are sensitive to the agent localities; thus this paper presents a novel idea for task allocation and load balancing in networked multiagent systems, which takes into account both the talents and centralities of agents. This paper first investigates the comparison between talent-based task allocation and centrality-based one; then, it explores the load balancing of such two approaches in task allocation. The experiment results show that the centrality-based method can reduce the communication costs for single task more effectively than the talent-based one, but the talent-based method can generally obtain better load balancing performance for parallel tasks than the centrality-based one.     

Contextual Resource Negotiation-Based Task Allocation and Load Balancing in Complex Software Systems

In the complex software systems, software agents always need to negotiate with other agents within their physical and social contexts when they execute tasks. Obviously, the capacity of a software agent to execute tasks is determined by not only itself but also its contextual agents; thus, the number of tasks allocated on an agent should be directly proportional to its self-owned resources as well as its contextual agents' resources. This paper presents a novel task allocation model based on the contextual resource negotiation. In the presented task allocation model, while a task comes to the software system, it is first assigned to a principal agent that has high contextual enrichment factor for the required resources; then, the principal agent will negotiate with its contextual agents to execute the assigned task. However, while multiple tasks come to the software system, it is necessary to make load balancing to avoid overconvergence of tasks at certain agents that are rich of contextual resources. Thus, this paper also presents a novel load balancing method: if there are overlarge number of tasks queued for a certain agent, the capacities of both the agent itself and its contextual agents to accept new tasks will be reduced. Therefore, in this paper, the task allocation and load balancing are implemented according to the contextual resource distribution of agents, which can be well suited for the characteristics of complex software systems; and the presented model can reduce more communication costs between allocated agents than the previous methods based on self-owned resource distribution of agents.     

Preface

Networked sensing and control has attracted significant interest in recent years due to its wide applications. For example, sensor networks, especially wireless sensor networks, have found important applications in environmental monitoring, agriculture, building and industrial automation, machine condition monitoring, intelligent transportation systems, health care, surveillance, and defense. On the other hand, due to the flexibility and significant cost-saving, there has been an increasing trend for control systems to utilize digital communication networks for exchanging information between sensor and controller and/or controller and actuator, as well as between subsystems or systems sharing the same communication networks. Furthermore, networked multiagent systems, where the collective behavior of a system is realized through interactions among dynamically decoupled systems, have also been the focus of many recent research efforts. In networked systems, the network is an important component with constraints and uncertainties, such as the limited bandwidth, random transmission-delays, possible packet-losses, out-of-sequence measurements, etc. The uncertainties and constraints become more significant in wireless networks because of its limited resources of communication and computation, and the fading that changes the throughput of communication channels. As a result, there is an imperative need of new tools for analysis and synthesis, and new algorithms for control, estimation and decision-making, to take these uncertainties and constraints into consideration and handle the interplay among communication, computation and control. In the past few years, a lot of research efforts have been devoted to this field and significant advancement has been made. This special issue intends to reflect part of those achievements. Indeed, the special issue consists of 17 papers among which 8 discuss the control over communication networks, 1 investigates the traffic control in communication networks, 3 are concerned with multiagent systems, and 5 deal with wireless sensor networks. All papers were selected from numerous submissions and carefully reviewed.We hope that the special issue will promote the research interest in networked sensing, actuation and control systems, and foster new applications of networked systems. Special issue editors: Lihua Xie, Nanyang Technological University, Singapore. Frank L. Lewis, The University of Texas at Arlington, USA.     

基于面向对象着色Petri网的多Agent系统建模

提出了一种基于面向对象着色Petri网（OOCPN）的多Agent建模方法,与其它建模方法相比,OOCPN可以全面地刻画出Agent的个体行为特征和多Agent间复杂、并行的动态交互,讨论了利用OOCPN进行个体Agent和多Agent间交互协议的建模,并通过对网上智能购物系统的实例分析,展示了OOCPN对多Agent系统的建模能力。     

网络环境下多机器人的任务分配实现

多机器人协调系统中,任务分配机制的合理与否直接决定了系统中的各机器人的工作效率。该文在分析了MAS系统中任务分配的几种方法后,提出了以任务分配的一种———协商机制为基础,用网络通讯来实现多机器人间任务的分配模型,以便合理地分配任务。试验证明方法行之有效。     

基于势博弈的异构多智能体系统任务分配和重分配EI北大核心CSCD

针对异构多智能体系统,基于势博弈理论提出一种新的任务分配和重分配算法.考虑任务执行同步性和任务时效性的多重约束,导致异构多智能体系统中各个体任务执行时间受到多种限制,建立一个基于势博弈的算法结构,使系统以分布式方式工作.在此基础上,基于势博弈理论设计任务分配算法,保证在较低复杂度的同时,可以得到近似最大化期望全局效用的良好分配方案,并且随后将所提出的方法推广到任务重分配方案实现故障下的容错.最后,针对攻击任务场景对所提算法进行仿真验证,结果表明,在期望全局效用、容错能力和算法复杂度方面具有全面的性能.     

Self-organizing neural architectures and cooperative learning in a multiagent environment.

Temporal-Difference-Fusion Architecture for Learning, Cognition, and Navigation (TD-FALCON) is a generalization of adaptive resonance theory (a class of self-organizing neural networks) that incorporates TD methods for real-time reinforcement learning. In this paper, we investigate how a team of TD-FALCON networks may cooperate to learn and function in a dynamic multiagent environment based on minefield navigation and a predator/prey pursuit tasks. Experiments on the navigation task demonstrate that TD-FALCON agent teams are able to adapt and function well in a multiagent environment without an explicit mechanism of collaboration. In comparison, traditional Q-learning agents using gradient-descent-based feedforward neural networks, trained with the standard backpropagation and the resilient-propagation (RPROP) algorithms, produce a significantly poorer level of performance. For the predator/prey pursuit task, we experiment with various cooperative strategies and find that a combination of a high-level compressed state representation and a hybrid reward function produces the best results. Using the same cooperative strategy, the TD-FALCON team also outperforms the RPROP-based reinforcement learners in terms of both task completion rate and learning efficiency.     

Distributed clustering for group formation and task allocation in multiagent systems: A swarm intelligence approach

Most clustering methods rely on central data structures and/or cannot cope with dynamically changing settings. Besides, these methods need some hints about the target clustering. However, issues related to the current use of Internet resources (distribution of data, privacy, etc.) require new ways of dealing with data clustering. In multiagent systems this is also becoming an issue as one wishes to group agents according to some features of the environment in order to have agents accomplishing the available tasks in an efficient way. In this paper we discuss the application of a clustering algorithm that is inspired by swarm intelligence techniques such as organization of bee colonies and task allocation among social insects. This application involves a complex task allocation scenario, the RoboCup Rescue, where tasks with different characteristics must be allocated to agents with different capabilities. Our results have shown that clustering agents is effective in this scenario as agents act in a more coordinated way.     

Global bounded consensus of multiagent systems with nonidentical nodes and time delays

This paper investigates the global bounded consensus problem of networked multiagent systems consisting of nonlinear nonidentical node dynamics with the communication time-delay topology. We derive globally bounded controlled consensus conditions for both delay-independent and delay-dependent conditions based on the Lyapunov-Krasovskii functional method. The proposed consensus criteria ensure that all agents eventually move along the desired trajectory in the sense of boundedness. Meanwhile, the bounded consensus criteria can be viewed as an extension of the case of identical agent dynamics to the case of nonidentical agent dynamics. We finally demonstrate the effectiveness of the theoretical results by means of a numerical simulation.     

基于迁移学习的多任务分配算法

对于多任务分配问题，传统的方法针对每一个任务独立地寻找一个最优分配方案，没有考虑任务间的关联以及历史经验对新任务分配的影响，因而复杂度较高。研究了多智能体系统中的多任务分配问题，通过迁移学习来加速任务分配以及子任务的完成。在分配目标任务时，通过计算当前任务和历史任务的相似度找到最适合的源任务，再将源任务的分配模式迁移到目标任务中，并在完成子任务的过程中使用迁移学习，从而提高效率，节约时间。最后，通过“格子世界”的实验证明了该算法在运行时间和平均带折扣回报方面都优于基于Q学习的任务分配算法。