A multi-agent approach for dynamic channel allocation

We propose a multi‐agent approach for dynamic channel allocation (MA‐DCA) in mobile cellular networks. Our approach assumes that each cell in a cellular network works as an agent that negotiates its bandwidth (channel) requirements with its neighbors to minimize its call drop probability. Using stochastic simulation, we compare our MA‐DCA approach with simple fixed channel allocation (FCA) and dynamic channel borrowing approaches for different call arrival rates, cellular network sizes and number of available channels. The results of our experiments show that the proposed MA‐DCA approach performs better than dynamic channel borrowing and FCA approaches.     

低约束密度分布式约束优化问题的求解算法

多Agent协作过程中的许多挑战都可以建模为分布式约束优化问题.针对低约束密度的分布式约束优化问题,提出了一种基于贪婪和回跳思想的求解算法.在该算法中,各Agent基于贪婪原则进行决策,能够利用低约束密度问题中大量赋值组合代价为0这一特点来加快求解速度.同时,Agent间的回跳机制可以在贪婪原则陷入局部最优时保证算法的完全性.相对于已有主流算法,该算法可以在保持多项式级别的消息长度/空间复杂度的前提下,以较少的消息数目求解低约束密度的分布式约束优化问题.给出了算法关键机制的正确性证明,并通过实验验证了算法的上述性能优势.     

基于开放式多智能体结构的分布式自主机器人系统

针对多机器人系统的分布式自主控制,本文首先提出了一种开放式的多智能体结构,给出了 设计原则和技术特点．然后面向真实世界的多机器人实时协作任务,采用多台自主移动机器 人构造了一个多机器人系统, 该系统集成了包括机器人视觉、传感器融合、无线通讯网络 以及基于行为控制等多项技术．最后采用基于行为融合的加权方法,实现了多机器人的编队 控制,实验结果表明了上述体系结构与方案的有效性．     

具有群集行为的时变函数分布式优化

本文对具有群集行为的连续时间多智能体系统的优化问题进行了研究. 考虑具有二阶动力学的多智能体系统, 每个智能体都具有一个局部的时变代价函数. 本文的目标是仅仅依靠局部信息交流使得多智能体在运动的过程中保持 连通性、避免碰撞、总体代价函数最小. 为此本文设计了一种具有群集行为的控制协议, 该协议仅仅依赖于自己和邻居 的速度. 可以证明在该控制协议作用下, 所有智能体在保持连通、避免碰撞的同时, 速度能够跟踪上最优速度. 最后, 通 过一个仿真来说明本文的结果.     

An integrated intelligent process planning system (IIPPS) for machining

A key component of computer integrated manufacturing (CIM) is computer aided process planning (CAPP). Process planning in machining involves the determination of the cutting operations and sequences, the selection of machine tools and cutting tools, the calculation of machining parameters, and the generation of CNC part programs. Industrial structures in Norway are defined as small and medium-sized companies. The important fact is how well these companies use high technologies and resources in order to improve their production efficiency, product quality, and company competition in international markets. The concept of an integrated intelligent system (IIS) is created for this purpose. A prototype system, the integrated intelligent process planning system (IIPPS), is described for machining; it was developed on the basis of an IIS and constructed using three levels of effort: (1) AutoCAD, (2) dBASE III and (3) KnowledgePro. The system may be utilized not only by a process plann ing engineer in a company, but also by students of mechanical or industrial engineering.     

An Approximate Gradient Algorithm for Constrained Distributed Convex Optimization

In this paper, we propose an approximate gradient algorithm for the multi-agent convex optimization problem with constraints. The agents cooperatively compute the minimum of the sum of the local objective functions which are subject to a global inequality constraint and a global constraint set. Instead of each agent can get exact gradient, as discussed in the literature, we only use approximate gradient with some computation or measurement errors. The gradient accuracy conditions are presented to ensure the convergence of the approximate gradient algorithm. Finally, simulation results demonstrate good performance of the approximate algorithm.      

The analysis and performance evaluation of the pheromone-Q-learning algorithm

Abstract: The paper presents the pheromone‐Q‐learning (Phe‐Q) algorithm, a variation of Q‐learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe‐Q learning combines the standard Q‐learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe‐Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe‐Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe‐Q algorithm. The paper demonstrates the improved performance of cooperating Phe‐Q agents over non‐cooperating agents. The paper also shows how Phe‐Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone.     

Output feedback distributed optimization algorithms of second-order Lipschitz nonlinear multi-agent systems

This paper introduces output feedback distributed optimization algorithms designed specifically for second-order nonlinear multi-agent systems. The agents are allowed to have heterogeneous dynamics, characterized by distinct nonlinearities, as long as they satisfy the Lipschitz continuity condition. For the case with unknown states, nonlinear state observers are designed first for each agent to reconstruct agents' unknown states. It is proven that the agents' unknown states are estimated accurately by the developed state observers. Then, based on the agents' state estimates and the gradient of each agent local cost function, a kind of output feedback distributed optimization algorithms are proposed for the considered multi-agent systems. Under the proposed distributed optimization algorithms, all the agents' outputs asymptotically approach the minimizer of the global cost function which is the sum of all the local cost functions. By using Lyapunov stability theory, convex analysis, and input-to-state stability theory, the asymptotical convergence of the output feedback distributed optimization closed-loop system is proven. Simulations are conducted to validate the efficacy of the proposed algorithms.     

矩阵方程的分布式求解算法研究概述

近年来,随着大规模网络的兴起和分布式优化理论的广泛应用,矩阵方程的分布式求解算法研究也受到了越来越多的重视.矩阵方程的计算求解在理论和工程领域都有着重要的意义.在多智能体网络下的分布式计算问题中,矩阵方程中的数据信息按照各种方式进行划分,单个智能体只能够获取其中的一份数据,然后通过与其邻居智能体进行信息交互,最终合作求解出不同类型的符合方程要求的解.本文集中讨论了近几年来针对线性代数方程、几类不带约束和带约束线性矩阵方程、以及其他矩阵相关的分布式计算和求解问题,介绍了投影一致方法、转化成分布式优化问题再求解的方法、以及针对特殊矩阵如稀疏矩阵的信息传递方法等分布式算法设计方法.最后,简要总结全文以及对分布式矩阵计算方向的研究进行了展望.     

工程约束表示模型与求解算法研究

分析了工程设计中约束问题的特点和规律,阐明了工程约束与几何约束在工程ＣＡＤ领域中的表现形式,从一般工程意义上提出了符合工程特点的“多元约束图”的约束表示模型。基于“多元约束图”模型,文章提出了“最小约束度优先和最小值域范围优先”的约束求解算法,描述了针对该算法的约束传播层的设计思想,将约束变量有序地形成一单向依赖关系表,有效地表达了约束一致性的思想,较大规模地减少了搜索空间,该研究应用于工厂钢结构     

自组织分治求解分布式约束优化问题

分布式约束优化问题(DCOP)是在大规模、开放、动态网络环境中的优化问题,在计算网格、多媒体网络、电子商务、企业资源规划等领域中都有广泛应用.除了具有传统优化问题的非线性、约束性等特点,DCOP还具有动态演化、信息区域化、控制局部化、网络状态异步更新等特点.寻求一种解决DCOP的大规模、并行、具有智能特征的求解方法已成为一个具有挑战性的研究课题.目前已提出多种求解DCOP的算法,但大多不是完全分散的算法,存在集中环节,需要网络的全局结构作为输入,不适合处理由规模巨大、地理分布、控制分散等因素导致的全局结构难以获取的分布式网络.针对该问题,提出一个基于自组织行为的分治策略求解DCOP.在不具有全局网络知识的情况下,分布在网络中的多个自治Agent基于局部感知信息、采用自组织的方式协作求解.与已有算法相比,它是一个完全分散式算法,并在求解效率和求解质量方面都展现出很好的性能.     

具有通信时延和有向网络的多智能体分布式优化

本文对非平衡有向拓扑下一阶多智能体系统的分布式优化问题进行研究.研究的智能体在与邻居的通信过程中都有一个变化的时延.本文的目标是找到使得目标函数f(x)=(NΣi=1)fi(xi)最小的智能体的状态.提出了一种基于有向图的拉普拉斯矩阵的零特征值对应的左特征向量和智能体的局部信息的控制器.在这项研究中,去掉了fi(xi)...     

Global consensus of second-order unknown multi-agent systems: A distributed fuzzy iterative learning scheme

This article focuses on global fuzzy consensus control of unknown second-order nonlinear multi-agent systems based on adaptive iterative learning scheme. In order to achieve global consensus, a replacement idea is introduced, where fuzzy systems are used as feedforward compensators to model unknown nonlinear dynamics relying on tracking signals. Considering that the network communication is distributed, a kind of hybrid control protocol is designed to avoid the complete dependence on the tracking signals. In addition, considering the complexity of the external environment, this article extends the above distributed protocol to the case of unknown control directions to study global consensus. Finally, the feasibility of the proposed protocols is verified by Matlab numerical simulations.     

Multi-Agent Constraint Systems with Preferences: Efficiency, Solution Quality, and Privacy Loss

In this paper, we consider multi-agent constraint systems with preferences, modeled as soft constraint systems in which variables and constraints are distributed among multiple autonomous agents. We assume that each agent can set some preferences over its local data, and we consider two different criteria for finding optimal global solutions: fuzzy and Pareto optimality. We propose a general graph-based framework to describe the problem to be solved in its generic form. As a case study, we consider a distributed meeting scheduling problem where each agent has a pre-existing schedule and the agents must decide on a common meeting that satisfies a given optimality condition. For this scenario we consider the topics of solution quality, search efficiency, and privacy loss, where the latter pertains to information about an agent's pre-existing meetings and available time-slots. We also develop and test strategies that trade efficiency for solution quality and strategies that minimize information exchange, including some that do not require inter-agent comparisons of utilities. Our experimental results demonstrate some of the relations among solution quality, efficiency, and privacy loss, and provide useful hints on how to reach a tradeoff among these three factors. In this work, we show how soft constraint formalisms can be used to incorporate preferences into multi-agent problem solving along with other facets of the problem, such as time and distance constraints. This work also shows that the notion of privacy loss can be made concrete so that it can be treated as a distinct, manipulable factor in the context of distributed decision making.     

Internet-based manufacturing: A review and a new infrastructure for distributed intelligent manufacturing

Global competition and rapidly changing customer requirements are forcing major changes in the production styles and configuration of manufacturing enterprises. Traditional centralized manufacturing systems are not able to meet these requirements. In recent years, the internet has become the worldwide information platform for the sharing of information and data. Information processing is an important challenge in an internet-based manufacturing environment, and must facilitate distribution, heterogeneity, autonomy and cooperation. This paper reviews recent trends and major developments in internet-based manufacturing. Broad categories of distributed information management are identified, based upon the development of computer integrated manufacturing, and the possible direction of future research is indicated.     

基于实例图形的几何约束满足策略

以实例图形概念，应用稀疏矩阵三角块分技术及非线性数值迭代技术，提出了有效的包括几何约束表达、有效性检验、约束分解及求解的约束满足策略。该策略还支持不完备几何约束的约束满足。     

基于实例图形的几何约束满足策略

以实例图形概念，应用稀疏矩阵三角块分技术及非线性数值迭代技术，提出了有效的包括几何约束表达、有效性检验、约束分解及求解的约束满足策略。该策略还支持不完备几何约束的约束满足。     

一种基于修改的约束满足算法

求解约束满足问题的修改算法从实始的有冲突的完整解出发，不断修改理有的变量赋值，从而得到无冲突的完整解。本文将启发式方法应用了修改型算法，提出了一种高效的基于修改的约束满足算法。     

基于梯度博弈的网络化软件优化机制

为了提高服务效率和实现更多样的功能,越来越多的软件系统选择将业务或服务部署在不同的物理设备上,使用互联网通信协作,这类软件系统被称为网络化软件,然而此类软件高度分布的特点为系统的调控带来了难题.基于博弈理论解决网络化软件的优化决策问题,让系统中的软件节点交换信息,并根据收益函数调整自身状态,实现系统的整体优化;同时,通过多智能体一致性理论克服优化过程中可能存在通信的问题,让软件节点使用不完全的系统信息做出决策;此外,提出了自适应步长机制和强制协调机制,基于节点间的估计误差值对部分参数进行合理调整,有效缓解了此类方法容易发散、参数选择困难的问题,实现了状态寻优和估计误差修正间的有效协同,提高了方法的收敛速度.     

MetaMorph II: an agent-based architecture for distributed intelligent design and manufacturing

Agent technology derived from Distributed Artificial Intelligence is increasingly being considered for next generation computer-integrated manufacturing systems, to satisfy new requirements for increased integrability, configurability, adaptability, extendibility, agility, and reliability. This paper reviews our previous research on the application of the agent-based technology to intelligent design and manufacturing and describes the current research project MetaMorph II (an agent-based architecture for distributed intelligent design and manufacturing).