基于多Agent的特征提取模型研究

讨论了基于多Agent的特征提取模型的工作流程,主要研究了该多Agent模型的构建框架和模型中单个Agent的通用结构,借助JAFMAS这一面向MAS的基于Java的Agent框架实现了系统的建立,并最后给出了所举例子的实际运行结果。     

层次结构MAS模型的设计与实现

Agent技术被越来越多地用于在不同领域开发软件系统,多Agent模型的研究已经成为热点。本文提出层次结构多Agent模型概念,结合一个实例,论述了层次结构MAS模型的基本思想和结构框架,研究了层次结构MAS模型的设计与实现过程。     

基于政策的层次系统协作模型

为了解决大型层次系统中多Agent间的协作问题,提出了一种基于政策的协作模型。首先分析层次系统中Agent间协作的特点,然后给出相关概念定义,构建Agent政策框架及政策分享机制,最后提出协作模型算法。实践表明,该模型能够在大型、分布式环境中实现资源和服务的协同,使Agent的行为决策更加规范可控,因此具有高度的灵活性及扩展性,对于解决层次MAS协作问题具有一定的参考价值。     

基于交互的Agent行为选择动态性分析

在动态不可预知环境下,Agent行为选择过程表现出动态性。该文基于人工生命和MAS框架,建立Agent行为选择动态系统模型,运用动态系统观点研究Agent感知、认知交互和适应,分析Agent行为选择适应外部环境和满足内部需求的动态平衡过程。在仿真试验中,通过21个Agent局部感知和交互,实现了全局平衡、稳定的群体运动行为。     

基于 Aglets的MAS信用研究的实验平台设计

本文在Aglet平台上开发出一个MAS可视化实验平台。该试验平台主要用于MAS系统(Multi-agentSystems)用机制研究。用户在该平台上能够方便快捷地配置出一个多Agent系统,该系统不但具有Agent与Agent,Agent实体与平台的通信,Agent与用户的交互,Agent在内外网的移动等基本功能,而且可以在Agent之间建立信用关系,可以存储记录和直观的显示交互的历史及信用信息。该系统是一个开放的系统,用户可以方便的在该系统中加入自己的安全构架,信用策略和信用相关算法。以方便快速测试新的MAS信用模型。     

基于Aglets的MAS信用研究的实验平台设计

本文在Aglet平台上开发出一个MAS可视化实验平台。该试验平台主要用于MAS系统 (Multiagent Systems)用机制研究。用户在该平台上能够方便快捷地配置出一个多Agent系统,该系统不但具有Agent与Agent, Agent实体与平台的通信,Agent与用户的交互,Agent在内外网的移动等基本功能,而且可以在Agent之间建立信用关系,可以存储记录和直观的显示交互的历史及信用信息。该系统是一个开放的系统,用户可以方便的在该系统中加入自己的安全构架, 信用策略和信用相关算法。以方便快速测试新的MAS信用模型。     

知识获取模型的设计与探讨

介绍了MAS的组成和BDI Agent的结构,在此基础上提出了基于动态信任的MAS知识获取模型,并给出了基于模态逻辑的知识获取形式化框架,最后用形式化框架对知识获取模型进行了描述.     

一种基于情境演算的进化MAS系统

给出了一个基于情境演算的多Agent系统(MAS)建模的框架,可以帮助更好地理解和分析MAS的静态结构和动态行为,尤其是意向驱动将MAS中相互独立的元素包括实体、活动等关联起来形成MAS系统,能够随着环境的变化驱动MAS进化.     

多Agent的交互研究

Agent间进行协作的基础是交互,采用何种交互模型关系到能否有效地实现Agent的交互能力,RASA是2007年Tim Miller提出的一个MAS（multi-agent system）交互协议框架,这个框架已被证明可以更好的促进MAS的交互,并通过一个协商协议实例来展现RASA良好的特性。     

智能控制中的多Agent系统

根据智能控制的安全、经济和舒适三大目标,提出一种新的基于BACnet(楼宇自动控制网络数据通信协议)的MAS(多Agent系统)框架．此框架通过不同层次、不同功能的Agent的自治能力及协作协调,使得智能控制系统的各个相对分散的子系统能够有效地结合在一起．全局Agent负责整体功能的实现,而每个子系统均设置自身的Agent组．此外,还提出了基于MAS框架的SAS(安防自动化系统)逻辑结构,同时遵循FIPA(智能物理Agent基金)标准设计了此MAS系统的Agent通信语言,并对SAS系统进行了重点分析和研究,包括CCTV(闭路电视监控)系统和基于Auto-ID(自动识别)的系统．     

Participant Selection for Short-term Collaboration in Open Multi-agent systems

In multi-agent systems, agents coordinate their behaviour and work together to achieve a shared goal through collaboration. However, in open multi-agent systems, selecting qualified participants to form effective collaboration communities is challenging. In such systems, agents do not have access to complete domain knowledge, they leave and join the systems unpredictably. More importantly, agents are mostly self-interested and have multiple goals and policies that may be even conflicting with others, which makes the participant selection even more challenging.Many of the current approaches are not applicable in constantly evolving open systems, where their performance will be affected by any unpredictable behaviour, agents’ lack of complete domain knowledge and the impossibility of having a central coordinator agent. In open systems, agents require a mechanism that enables them to dynamically change their perception of the environment and observe their neighbouring agents, so that they can identify qualified collaboration participants that have no conflicting goals and to balance their level of cooperation and self-interest.In this paper, we propose OPSCO, as a solution for On-demand Participant Selection for Short-term Collaboration in Open multi-agent systems. Unlike the existing research, we do not assume any predefined setting for agents’ structure in the system and do not have access to complete domain knowledge and allow each agent to build a dynamic dependency model and maintain when there is a change in the system. The model captures the agent’s most recent dependency structure of goals and policies with its neighbouring agents. It enables them to identify and select a qualified non-conflicting set of participants.OPSCO is evaluated in a real world open system smart grid and constrained resource sharing case studies. OPSCO outperforms other methods by selecting a qualified non-conflicting set of agents to collaborate. OPSCO balances the self-interest and level of cooperation and decreases failure in the overall agents’ goals (individual/shared).     

Fuzzy agent-based expert system for steel making process

In this paper, a fuzzy multi-agent system is proposed for electric arc furnace steel making processes. Each process of electric arc furnace steel making is assigned to be an agent, which works independently whilst coordinates and cooperates with other acquaintance agents. Adaptive neuro-fuzzy inference system is used to generate agents’ knowledge bases. Contract net protocol is used as negotiation protocol in the proposed multi-agent system.     

Reaching a consensus in networks of high-order integral agents under switching directed topologies

Consensus problem of high-order integral multi-agent systems under switching directed topology is considered in this study. Depending on whether the agent’s full state is available or not, two distributed protocols are proposed to ensure that states of all agents can be convergent to a same stationary value. In the proposed protocols, the gain vector associated with the agent’s (estimated) state and the gain vector associated with the relative (estimated) states between agents are designed in a sophisticated way. By this particular design, the high-order integral multi-agent system can be transformed into a first-order integral multi-agent system. Also, the convergence of the transformed first-order integral agent’s state indicates the convergence of the original high-order integral agent’s state, if and only if all roots of the polynomial, whose coefficients are the entries of the gain vector associated with the relative (estimated) states between agents, are in the open left-half complex plane. Therefore, many analysis techniques in the first-order integral multi-agent system can be directly borrowed to solve the problems in the high-order integral multi-agent system. Due to this property, it is proved that to reach a consensus, the switching directed topology of multi-agent system is only required to be ‘uniformly jointly quasi-strongly connected’, which seems the mildest connectivity condition in the literature. In addition, the consensus problem of discrete-time high-order integral multi-agent systems is studied. The corresponding consensus protocol and performance analysis are presented. Finally, three simulation examples are provided to show the effectiveness of the proposed approach.     

Consensus in multi-agent systems with nonlinear uncertainties under a fixed undirected graph

This paper presents consensus algorithms by integrating cooperative control and adaptive control laws for multi-agent systems with unknown nonlinear uncertainties. An ideal multi-agent system without uncertainties is introduced first. The cooperative control law, based on an artificial potential function, is designed to make the ideal multi-agent system achieve consensus under a fixed and connected undirected graph. The presence of uncertainties will degenerate the performance, or even destabilize the whole multi-agent system. The L ₁ adaptive control law is therefore introduced to handle unknown nonlinear uncertainties. Two different consensus cases are considered: 1) normal consensus—where all agents reach an agreement on an initially undetermined position and velocity, and 2) consensus with a virtual leader—where all agents’ states converge to the virtual leader’s states. Under a fixed and connected undirected graph, the presented consensus algorithms enable the real multi-agent system to stay close to the ideal multi-agent system which achieves consensus with or without a virtual leader. Simulation results of 2-D consensus with nonlinear uncertainties are provided to demonstrate the presented algorithms.     

具有时滞的混合阶多智能体系统的组一致性

针对由离散时间一阶和二阶智能体组成的混合阶多智能体系统,研究其在固定和切换拓扑结构下受通信时滞影响时的组一致性问题。分别为两类智能体提出组一致性协议,引入模型变换,将闭环系统转化为等价系统。在一定假设条件下,以代数图论、矩阵理论为主要研究工具,分别在固定和切换拓扑结构下给出了混合阶多智能体系统实现渐近组一致性的条件。采用数值仿真对所得结果的有效性进行了验证。     

多Agent系统安全性分析与设计

全面分析了多Agent系统的安全特性、安全风险和安全需求.在此基础上,研究了多Agent系统多层安全模型及安全多Agent系统需要提供的基础安全服务.基于FIPA-OS框架,设计并实现了一个面向Agent的二级认证PKI系统,同时提出一组安全服务Agent,为FIPA-OS框架增加了的身份鉴别、证书管理、安全审计、资源访问控制及Agent社区管理等多种安全机制.     

A multi-agent reinforcement learning approach to robot soccer

In this paper, a multi-agent reinforcement learning method based on action prediction of other agent is proposed. In a multi-agent system, action selection of the learning agent is unavoidably impacted by other agents’ actions. Therefore, joint-state and joint-action are involved in the multi-agent reinforcement learning system. A novel agent action prediction method based on the probabilistic neural network (PNN) is proposed. PNN is used to predict the actions of other agents. Furthermore, the sharing policy mechanism is used to exchange the learning policy of multiple agents, the aim of which is to speed up the learning. Finally, the application of presented method to robot soccer is studied. Through learning, robot players can master the mapping policy from the state information to the action space. Moreover, multiple robots coordination and cooperation are well realized.     

Compositional Verification of a Multi-Agent System for One-to-Many Negotiation

Verification of multi-agent systems hardly occurs in design practice. One of the difficulties is that required properties for a multi-agent system usually refer to multi-agent behaviour which has nontrivial dynamics. To constrain these multi-agent behavioural dynamics, often a form of organisational structure is used, for example, for negotiating agents, by following strict protocols. The claim is that these negotiation protocols entail a structured process that is manageable with respect to analysis, design and execution of such a multi-agent system. In this paper this is shown by a case study: verification of a multi-agent system for one-to-many negotiation in the domain of load balancing of electricity use. A compositional verification method for multi-agent systems is applied that allows to (1) logically relate dynamic properties of the multi-agent system as a whole to dynamic properties of agents, and (2) logically relate dynamic properties of agents to properties of their subcomponents. Given that properties of these subcomponents can be verified by more standard methods, these logical relationships provide proofs of the dynamic properties of the multi-agent system as a whole.     

基于多agent的军事物流系统仿真建模方法*

针对军事物流系统复杂性和不确定性的特点,提出了一种基于多agent的系统仿真建模方法。采用中间到两端的多层agents组织方式,引入agent分区概念对agent行为分别进行详细描述。在基于中间层agents逻辑模型的基础上抽象上层agents和下层agents。通过中间层各agent的活动映射而构造的下层agents负责特定的事务处理。中间层agents负责协调下层agents的信息传递,并与上层agents进行交互;上层agents负责协调中间层agents之间的交互。给出了系统模型的仿真实验方法,最后建立了基于多agent的军事物流仿真模型,并通过系统模型的仿真运行,对系统模型的正确性进行了检验。     

Multi-agent systems with reinforcement hierarchical neuro-fuzzy models

This paper introduces a new multi-agent model for intelligent agents, called reinforcement learning hierarchical neuro-fuzzy multi-agent system. This class of model uses a hierarchical partitioning of the input space with a reinforcement learning algorithm to overcome limitations of previous RL methods. The main contribution of the new system is to provide a flexible and generic model for multi-agent environments. The proposed generic model can be used in several applications, including competitive and cooperative problems, with the autonomous capacity to create fuzzy rules and expand their own rule structures, extracting knowledge from the direct interaction between the agents and the environment, without any use of supervised algorithms. The proposed model was tested in three different case studies, with promising results. The tests demonstrated that the developed system attained good capacity of convergence and coordination among the autonomous intelligent agents.