基本DFL的多Agent协调工作机制研究

多agent的协调性是多agent系统与其它相关研究领域区别开来的关键性问题之一,也是保证多个智能系统能在一起共同工作的关键.由于agent它自身是一个主体,就其本质上讲是一个动态的客体,因此动态性和模糊性是agent的主要特性,而多agent之间这个问题体现的就更为突出.因此,该文基于DFL对多agent协调工作机制进行研究,给出agent的能力表现模型、能力扩展模型等内容,为人们解决这方面的问题提供理论依据.     

An emergency aircraft evacuation simulation considering passenger emotions

The evacuation behavior of passengers was formulated as an autonomous agent and multi-agent model (AAMAS) evolving over a two-dimensional grid cell that represents aircraft cabins and passengers. In this model, the autonomous agents are initially placed in seat squares and move toward an emergency exit after an aircraft accident occurs. The autonomous agent mimics the behavior of passengers in the cabin, who must not only view their surroundings to collect the useful information but also select a route to an emergency exit. As the situation evolves, the agents feel the mental stress or strong fear or anxiety; thereby reacting unfavorably in the situation that they panic at. This abnormal evacuation behavior of panic agents generates time delays in the evacuation flow towards the exits. Therefore, such panic and its effect on evacuation behavior should be considered as an important factor in evacuation simulations. In this paper, it is supposed that the level of panic depends on three factors: remaining time, frequency of waiting and the difficulty of finding an exit. The dependencies of these factors on the time needed to complete an evacuation and the number of the panic agents in the aircraft were determined by the simulation. Considering the simulation results and situations of the actual aircraft accident “Garuda Indonesia Airways Accident”, it was possible to develop aircraft evacuation scenarios that considered passenger emotions.     

An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing

Agent technology is considered as a promising approach for developing optimizing process plans in intelligent manufacturing. As a bridge between computer aided design (CAD) and computer aided manufacturing (CAM), the computer aided scheduling optimization (CASO) plays an important role in the computer integrated manufacturing (CIM) environment. In order to develop a multi-agent-based scheduling system for intelligent manufacturing, it is necessary to build various functional agents for all the resources and an agent manager to improve the scheduling agility. Identifying the shortcomings of traditional scheduling algorithm in intelligent manufacturing, the architecture of intelligent manufacturing system based on multi-agent is put forward, among which agent represents the basic processing entity. Multi-agent-based scheduling is a new intelligent scheduling method based on the theories of multi-agent system (MAS) and distributed artificial intelligence (DAI). It views intelligent manufacturing as composed of a set of intelligent agents, who are responsible for one or more activities and interacting with other related agents in planning and executing their responsibilities. In this paper, the proposed architecture consists of various autonomous agents that are capable of communicating with each other and making decisions based on their knowledge. The architecture of intelligent manufacturing, the scheduling optimization algorithm, the negotiation processes and protocols among the agents are described in detail. A prototype system is built and validated in an illustrative example, which demonstrates the feasibility of the proposed approach. The experiments prove that the implementation of multi-agent technology in intelligent manufacturing system makes the operations much more flexible, economical and energy efficient.     

基于本体论的联邦式多主体系统设计

智能主体是一个多用途适应性的系统。它可以根据其环境执行不同的活动,动态地完成目标。多主体系统强调利用多个主体的能力完成复杂的任务。每个主体具有自治性,但当主体的数量增多时,主体间交互成为重要的问题。为了减轻主体交互的负担,作者采用联邦式的多主体系统结构。利用曾体为主体交互提供服务。并以本体论作为交互的基础。     

基于智能Agent的网络安全监控系统的研究

提出了一种称为IA－NSM（网络安全监控的智能Agent)的全新方案，它基于智能Agent技术，用于监控和管理Intranet环境下内部结点。IA－NSM是在传统的网络环境中集成了一个灵活的多Agent系统，从而加强了对受控结点非法行为的防范能力，介绍了基于智能Agent技术的网络安全监控系统的应用背景，系统功能，Agent分类和系统模型以及安全策略的制定原则。     

城市交通信号局部博弈交互下的学习协调控制

智能交通信号控制系统是智慧交通系统（intelligent traffic system,ITS）的重要组成部分,为形成安全高效的交通环境提供实时服务. 然而,现有自适应交通信号控制方法因通信受限难以满足复杂多变的交通需求. 针对通信时延长和信号灯有效利用率低的难题,提出一种基于边缘计算的异步决策的多智能体交通信号自适应协调方法（adaptive coordination method,ADM）. 该方法基于提出的端—边—云架构实现实时采集环境信息,将异步通信引入强化学习的多智能体协调过程,设计一种多智能体之间使用不同决策周期的异步决策机制. 实验结果表明边缘计算技术为高实时性要求的交通信号控制场景提供一种良好的解决思路,此外,相较于固定配时和独立决策的Q学习决策方法IQA（independent Q-learning decision algorithm）而言,ADM方法基于异步决策机制和邻居信息库实现智能体之间的协作,达到降低车辆平均等待长度及提高交叉口时间利用率的目标.

     

多智能体的分布式智能故障诊断

基于分布式人工智能的思想 ,将多Agent技术引入复杂故障诊断领域 ,分析了基于MAS的分布式智能故障诊断方法和过程 ;讨论了基于模式聚类的故障求解机制及对诊断问题任务辨识、分解 ;研究了多Agent宏观上的约束和关联 ;设计了应用Agent工作状态的表达机制 ;确定了应用Agent间的工作状态影响关系及多Agent间的交互、协作和通讯 ;构建了多Agent模糊关联模型 ;给出了多Agent诊断系统局部诊断决策与全局诊断决策的集成描述结构 ;建立了一种分布式Agent诊断系统结构及其原型系统 .在某电力企     

Type-2 fuzzy logic based urban traffic management

This paper presents a multi-agent system based on type-2 fuzzy decision module for traffic signal control in a complex urban road network. The distributed agent architecture using type-2 fuzzy set based controller was designed for optimizing green time in a traffic signal to reduce the total delay experienced by vehicles. A section of the Central Business District of Singapore simulated using PARAMICS software was used as a test bed for validating the proposed agent architecture for the signal control. The performance of the proposed multi-agent controller was compared with a hybrid neural network based hierarchical multi-agent system (HMS) controller and real-time adaptive traffic controller (GLIDE) currently used in Singapore. The performance metrics used for evaluation were total mean delay experienced by the vehicles to travel from source to destination and the current mean speed of vehicles inside the road network. The proposed multi-agent signal control was found to produce a significant improvement in the traffic conditions of the road network reducing the total travel time experienced by vehicles simulated under dual and multiple peak traffic scenarios.     

一种基于自适应复制的多Agent容错模型

为了构造和部署大规模的多agent系统,人们必须找到并解决其基本问题,其中之一就是可能存在的局部性系统故障。这也就意味着,容错对于大规模多agent系统来说,是一个无法回避的主题。文中讨论了这类问题并且提出了一种多agent系统的容错方法。最先的想法是将复制策略运用到agent中,对处于危急状态的agent进行复制从而避免系统故障,但是由于agent的危急性会在执行过程中演变,并且agent的可用资源是绑定的,所以需要动态以及自动地调整agent的复制体个数,从而最大化它们的作用和可靠性。文中将描述评估某个agent危险性的方法以及相关机制,并且决定使用何种策略(如:主动复制,被动复制)以及如何将其参数化(如:复制的个数)。     

多层建筑应急疏散仿真

以多层建筑为背景,研究应急疏散问题,基于改进的路径规划算法,并将多智能体技术应用在模型间的通信交流上.采用机器人感知周围环境,通过设置机器人个数以及初始位置,对灾情中室内被困人员进行智能搜救,并采集现场实时数据,作出决策分析.机器人实时感知现场状况的变化,引导人员疏散,并将实时数据传输给施救人员,采取进一步救援措施.结果表明,该三维仿真技术为有效减少人员疏散中的伤亡和最佳救援方案的制定提供了参考,具有一定的现实指导意义.     

一种等级式的多主体仿真框架

基于多主体的建模和仿真已经被广泛地应用到了复杂系统所涉及到的各个领域,系统中智能主体的实现直接影响系统的性能和仿真结果的有效性。本文通过分析反应主体和慎思主体,指出其各自的优点和缺陷,结合复杂系统仿真的实际情况,提出融合两种主体的多主体系统框架,并对实现中的关键问题给出了详细说明。     

A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations

Many computational tools for the simulation and design of emergency evacuation and egress are now available. However, due to the scarcity of human and social behavioral data, these computational tools rely on assumptions that have been found inconsistent or unrealistic. This paper presents a multi-agent based framework for simulating human and social behavior during emergency evacuation. A prototype system has been developed, which is able to demonstrate some emergent behaviors, such as competitive, queuing, and herding behaviors. For illustration, an example application of the system for safe egress design is provided.     

Cooperative, hybrid agent architecture for real-time traffic signal control

This paper presents a new hybrid, synergistic approach in applying computational intelligence concepts to implement a cooperative, hierarchical, multiagent system for real-time traffic signal control of a complex traffic network. The large-scale traffic signal control problem is divided into various subproblems, and each subproblem is handled by an intelligent agent with a fuzzy neural decision-making module. The decisions made by lower-level agents are mediated by their respective higher-level agents. Through adopting a cooperative distributed problem solving approach, coordinated control by the agents is achieved. In order for the multiagent architecture to adapt itself continuously to the dynamically changing problem domain, a multistage online learning process for each agent is implemented involving reinforcement learning, learning rate and weight adjustment as well as dynamic update of fuzzy relations using an evolutionary algorithm. The test bed used for this research is a section of the Central Business District of Singapore. The performance of the proposed multiagent architecture is evaluated against the set of signal plans used by the current real-time adaptive traffic control system. The multiagent architecture produces significant improvements in the conditions of the traffic network, reducing the total mean delay by 40% and total vehicle stoppage time by 50%.     

An immersive multi-agent system for interactive applications

This paper presents an interactive multi-agent system based on a fully immersive virtual environment. A user can interact with the virtual characters in real time via an avatar by changing their moving behavior. Moreover, the user is allowed to select any character as the avatar to be controlled. A path planning algorithm is proposed to address the problem of dynamic navigation of individual and groups of characters in the multi-agent system. A natural interface is designed for the interaction between the user and the virtual characters, as well as the virtual environment, based on gesture recognition. To evaluate the efficiency of the dynamic navigation method, performance results are provided. The presented system has the potential to be used in the training and evaluation of emergency evacuation and other real-time applications of crowd simulation with interaction.     

人群应急疏散中一种多智能体情绪感染仿真模型

为了从情绪的视角分析紧急情境下人群的疏散行为,梳理了现有情绪感染的研究工作,总结了人群紧急状况下行为特点.采用智能体描述人群个体,提出一种多智能体情绪感染模型.其主体框架分为感知层、情绪层、感染层、行为层和行动层.归纳了产生情绪感染现象的3个条件及情绪感染的3个规则,提出了情绪感染的算法,考虑个体的个性和个体间距离因素,采用情绪强度和人群紧密度来计算个体疏散速度.用C#语言编制了仿真实验,采用真实的地震疏散案例,验证了仿真疏散时间和实际观测的基本一致.通过与以往基于传染病思路的情绪感染模型对比,所提出的模型可以更好地描述情绪感染从局部到整体的过程.实验结果表明,所提出的模型可以推演情绪驱动下的群体聚集行为,有望为制定应急疏散预案提供一种可视化分析方法.     

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.     

ADK: An Agent Development Kit Based on a Formal Design Model for Multi-Agent Systems

The advent of multi-agent systems has brought us opportunities for the development of complex software that will serve as the infrastructure for advanced distributed applications. During the past decade, there have been many agent architectures proposed for implementing agent-based systems, and also a few efforts to formally specify agent behaviors. However, research on narrowing the gap between agent formal models and agent implementation is rare. In this paper, we propose a model-based approach to designing and implementing intelligent agents for multi-agent systems (MAS). Instead of using formal methods for the purpose of specifying agent behavior, we bring formal methods into the design phase of the agent development life cycle. Specifically, we use the formalism called agent-oriented G-net model, which is based on the G-net formalism (a type of high-level Petri net), to serve as the high-level design for intelligent agents. Based on the high-level design, we further derived the agent architecture and the detailed design for agent implementation. To demonstrate the feasibility of our approach, we developed the toolkit called ADK (Agent Development Kit) that supports rapid development of intelligent agents for multi-agent systems and we discuss the role of inheritance in agent-oriented development. As a potential solution for automated software development, we summarize the procedure to generate a model-based design of application-specific agents. Finally, to illustrate an application built on ADK, we present an air-ticket trading example.     

Web智能搜索多Agent系统结构及相关技术

提出了一种多Agent系统结构，由用户界面Agent、过滤查找Agent、反馈更新Agent、操作Agent、搜索Agent、搜索Agent和中心索引库6个部分组成。该多Agent结构适用于Internet上的智能搜索系统，能够完成信息主动搜索、中心索引、信息过滤查找以及用户反馈更新等功能。详细描述了多Agent系统的分工以及相关实现技术的研究。     

A genetic-fuzzy-neuro model encodes FNNs using SWRM and BRM

Genetic algorithms (GAs), fuzzy logic (FL), and neural networks (NNs) are frequently used artificial intelligence (AI) techniques. Since these three methods are complementary rather than competitive, many researchers have hybridized GAs, FL, and NNs to develop a better performance model. However, most hybrid models use a multistage combination or identify partial parameters required in the model resulting in sub-optimal solutions. This research fuses GAs, FL, and NNs to develop an evolutionary fuzzy neural inference model (EFNIM) that uses GAs to simultaneously search for all parameters required in fuzzy neural networks (FNNs). Two approaches, summit and width representation method (SWRM) and block-representation method (BRM), are proposed to encode variables in FL and NNs. Simulations are conducted to evaluate the performance of EFNIM. For different problems, membership functions (MFs) with the minimum FNN structure and optimal parameters of FNN are automatically and concurrently acquired using EFNIM. The research overcomes the difficulties faced in applying FL and NNs as well as saves efforts in trial-and-error experiments, questionnaire survey, interviews with experts, etc. Both prediction accuracy and time requirement for cost estimating are much improved by the proposed method.