Asynchronous Teams: Cooperation Schemes for Autonomous Agents

Experiments over a variety of optimization problems indicate that scale-effective convergence is an emergent behavior of certain computer-based agents, provided these agents are organized into an asynchronous team (A-Team). An A-Team is a problem-solving architecture in which the agents are autonomous and cooperate by modifying one another's trial solutions. These solutions circulate continually. Convergence is said to occur if and when a persistent solution appears. Convergence is said to be scale-effective if the quality of the persistent solution increases with the number of agents, and the speed of its appearance increases with the number of computers. This paper uses a traveling salesman problem to illustrate scale-effective behavior and develops Markov models that explain its occurrence in A-Teams, particularly, how autonomous agents, without strategic planning or centralized coordination, can converge to solutions of arbitrarily high quality. The models also perdict two properties that remain to be experimentally confirmed: construction and destruction are dual processes. In other words, adept destruction can compensate for inept construction in an A-Team, and vice-versa. (Construction refers to the process of creating or changing solutions, destruction, to the process of erasing solutions.) solution quality is independent of agent-phylum. In other words, A-Teams provide an organizational framework in which humans and autonomous mechanical agents can cooperate effectively.     

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with general sampling delay

In this paper we provide a unified framework for consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with a general sampling delay. First, a stochastic bounded consensus tracking protocol based on sampled data with a general sampling delay is presented by employing the delay decomposition technique. Then, necessary and sufficient conditions are derived for guaranteeing leader-follower multi-agent systems with measurement noises and a time-varying reference state to achieve mean square bounded consensus tracking. The obtained results cover no sampling delay, a small sampling delay and a large sampling delay as three special cases. Last, simulations are provided to demonstrate the effectiveness of the theoretical results.     

A novel model and behavior analysis for a swarm of multi-agent systems with finite velocity

Inspired by the fact that in most existing swarm models of multi-agent systems the velocity of an agent can be infinite,which is not in accordance with the real applications, we propose a novel swarm model of multi-agent systems where the velocity of an agent is finite. The Lyapunov function method and LaSalle’s invariance principle are employed to show that by using the proposed model all of the agents eventually enter into a bounded region around the swarm center and finally tend to a stationary state. Numerical simulations are provided to demonstrate the effectiveness of the theoretical results.     

Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance

The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system.     

Containment consensus with measurement noises and time-varying communication delays

In this paper,we consider the containment consensus control problem for multi-agent systems with measurement noises and time-varying communication delays under directed networks.By using stochastic analysis tools and algebraic graph theory,we prove that the followers can converge to the convex hull spanned by the leaders in the sense of mean square if the allowed upper bound of the time-varying delays satisfies a certain sufficient condition.Moreover,the time-varying delays are asymmetric for each follower agent,and the time-delay-dependent consensus condition is derived.Finally,numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results.     

市场导向、多主体协同与创新扩散:基于复杂网络的动态仿真

市场导向与多主体协同关系密切,是提升创新扩散效率的主要驱动要素。本文首先从协同关系和创新收益两个层面,构建空间结构和有效预期的演化机制,生成复杂网络模型,对多主体协同的创新扩散过程进行动态仿真。其次通过细化市场导向理论在用户需求、竞争驱动和职能协同等不同维度的作用路径,深入分析了市场导向对多主体协同的影响机制。研究表明:(1)市场导向对多主体协同的影响与网络结构动态特征具有高度相关性;(2)少量的用户需求与竞争驱动导向对多主体协同的效益提升最为显著,职能协同导向的影响则呈现周期性“倒U型”波动特征;(3)用户需求导向对多主体协同的创新收益驱动效应最明显,竞争驱动导向的推动效果次之。     

BP神经网络预测在流程企业供应链中的应用研究

本针对流程制造企业的管理模式和相应的供应链特点，采用多智能体技术建立基于预测的流程供应链模型。在比较预测方法的基础上，采用BP神经网络技术建立客户分销的预测支持系统，并采用某造纸流程企业的周销量件数进行了实例预测仿真。     

智慧社区运营的主体识别与社会网络分析

伴随着对社区治理的关注,如何通过提升社区的智能化保障居民生活的安全性、舒适度以及提升公众的参与水平已经成为当前的热点问题。传统的智慧社区运营管理理论仅考虑少数利益主体,并未关注多方利益主体,且大多研究未涉及多方利益主体的网络关系。基于此,从社区智慧运营“需求-供给-调控”等维度识别智慧社区运营的主体要素,深入调查智慧社区运营的利益主体,构建关系矩阵并绘制社会网络结构图,基于UCINET 6.0软件分析网络的密度、中心性以及影响力,识别关键因素并提出多维度的政策建议,为多方参与下的智慧社区协同发展提供理论支撑。     

具有竞争关系的多参与人委托代理问题研究

针对多委托人一多代理人委托代理问题,本文在多委托人之间和多代理人之间均存在竞争关系情形下,建立了非对称信息委托代理模型.分析表明,代理人努力水平对其产出的作用越敏感,代理人和委托人都越愿意付出更多努力;当委托人业务能力高低对代理人的影响较敏感时,委托人更愿意花心思去提高业务能力;委托一代理关系中的任何一方竞争过于激烈时会被处于委托一代理关系中的另一方利用.