基于协进化的多智能体系仿真框架及其面向对象设计与实现

协进化方法是继符号主义、行为主义方法之后出现的一种解决多智能体协作问题卓有成效的方法。本文提出基于协进化方法的多智能体体系和协调机制,建立了基于协进化机制的多智能体系统仿真框架,用面向对象的方法设计了仿真框架的类库体系,最后以“捕食者－猎物”问题为背景进行了实验研究。     

基于协进化和CPN的多智能体系统建模

多智能体系统体系结构及协作机制是多智能体理论研究的核心与热点问题,作为一种新兴的研究方法,基于协进化机制的多智能体协作具有广阔的研究前景。但已有的协进化模型过于简单,缺乏灵活性和更强的应用能力。CPN是多智能体系统建模的最好的工具之一,因此将其应用于协进化机制的多智能体系统的建模很有意义。     

基于协作协进化的多智能体机器人协作研究

协作问题一直是自主多智能体机器人系统研究的关键问题之一。基于多智能体机器人系统的CCP协作协议所生成的各智能体机器人的任务序列依赖于目标的初始顺序,因此难以得到最优解。文章提出了利用协作协进化来实现多智能体机器人之间协作的一种机制。该方法采用基于协作种群的技术来生成多智能体机器人任务执行序列,在给定的任务分解产生的所有可能解中寻找最优解,并通过交换局部知识和并行决策等手段来优化系统的性能。利用该机制,对3个智能体协作搬运8个物体进行计算机模拟,结果表明,该机制在优化任务执行序列方面作用明显,从而能有效提高多智能体机器人系统的性能。     

基于多智能体的作战模拟仿真模型框架研究

传统的作战模拟仿真模型大都是数学模型，难以刻画战争推进过程中所表现出来的复杂的战场态势变化及其内在的动力学机制。复杂适应系统理论和基于多智能体的建模仿真方法为作战模拟研究提供了新的思路。该文以复杂适应系统理论和基于多智能体的建模仿真方法为指导，提出了一种基于多智能体的作战模拟仿真模型的框架，着重分析了其中智能体的结构，对智能体的属性和行为进行了进一步的讨论。通过与面向对象的方法中对象概念的比较，认为面向对象的方法是实现基于多智能体的作战模拟仿真模型的有效途径。     

基于多智能体的无人作战多武器防御系统体系结构研究

重点以无人作战武器防御系统为背景,深入研究交互式智能体体系结构的应用,并对智能体的协调机制、通信机制、不完全全局规划方法,协进化方法等作了较深入的阐述。     

特钢厂炼钢-浇铸流程作业调度优化仿真模型

智能化的作业排产及调度问题是钢铁行业急需解决的难点,传统方法难以适应复杂多变的现场环境。针对这种情况提出一种基于多智能体理论的作业调度优化模型。模型定义针对钢铁行业的4类智能体,并提出智能体之间任务分配机制及上下游作业协调机制。仿真分析表明,所建模型能有效解决实际调度中炼钢和浇铸节奏协调性问题,使生产节奏更加紧密连贯,极大地提高了生产效率。该智能体模型以及任务协调机制框架具有普适性,为钢铁行业作业调度精细化、智能化的发展提供了可行性。     

基于仿真的企业竞争力研究

该文通过当前经济学发展动态的研究,针对经济系统的特征,提出用基于多智能体的仿真方法来研究经济系统中的问题,给出此方法的基本框架。并以企业竞争力为研究对象,研究了在复杂环境下的,采用不同招聘策略和工资发放策略的企业竞争力变化情况,验证了基于多智能体的仿真方法在经济管理中的作用。     

基于多智能体的企业产品定价研究

通过对当前经济学发展动态的研究，针对经济系统特征，提出一种研究经济系统中问题的实验方法——基于多智能体的仿真方法，给出此方法的基本框架。以企业产品定价为研究对象，研究了在复杂环境下，企业合理的定价原则，验证了基于多智能体的仿真方法在企业管理决策中的作用。     

基于消息通信的多智能体系统的应用

在基于多智能体的分布式实时仿真系统中,如何实现各Agent之间的通信与协调是一个核心问题。采用触发器消息传送方式为多智能体系统中的Agent建立了高效的通信机制,并通过分析机场地面交通中面临的问题构建了多智能体系统模型。实现了基于触发器消息通信机制的多智能体系统在机场地面交通仿真中的应用。     

求解SAT问题的多智能体社会进化算法

基于Agent社会合作机制以及智能体对环境的感知和反作用能力提出了一种新的求解SAT问题的多智能体社会进化方法MASEA(Multi-Agent Social Evolutionary Algorithm).该方法在多智能体进化思想的基础上,引入人类社会"关系网模型"的概念来建立智能体所能感知的邻域环境;同时在保留原有的竞争算子和自学习算子前提下,根据智能体具有竞争协作的特性,设计了一个新的算子——协作算子来共同完成整个进化过程.以标准SATLIB库中变量个数从20~250的3700个不同规模的标准SAT问题以及基于RB模型所产生的随机实例对MASEA的性能进行了全面的测试,并与其他一些具有较高性能算法的结果进行了比较.结果表明,MASEA具有更高的成功率和更高的运算效率.     

基于多Agent的天战系统建模与仿真方法研究

简单介绍了复杂系统理论及其建模方法，分析了天战系统的总体行为特征，指出对天战系统的仿真研究需采用复杂适应系统（CAS）理论的研究方法，即采用基于Agent的建模仿真方法学来开展研究，概述了Agent和多Agent系统的概念及基于多Agent的仿真，并给出了一个基于高层体系结构（HLA）的天战系统的仿真框架，最后展望了将来的研究工作。     

Modeling dynamic environments in multi-agent simulation

Real environments in which agents operate are inherently dynamic—the environment changes beyond the agents’ control. We advocate that, for multi-agent simulation, this dynamism must be modeled explicitly as part of the simulated environment, preferably using concepts and constructs that relate to the real world. In this paper, we describe such concepts and constructs, and we provide a formal framework to unambiguously specify their relations and meaning. We apply the formal framework to model a dynamic RoboCup Soccer environment and elaborate on how the framework poses new challenges for exploring the modeling of environments in multi-agent simulation.     

基于HLA的智能仿真支撑环境研究

引入了分布式人工智能（DAI）中多Agent的概念，将HLA仿真技术框架和多Agent技术相结合，提出了一种具有智能特性的仿真支撑环境，实现了一个通用的Agent仿真对象模型，介绍了如何利用Agent通信语言KQML进行互操作的方法，描述了仿真系统的工作过程。     

COMPONENT-BASED MODELS AND SIMULATIONS FOR SUPPORTING VALID MULTI-AGENT SYSTEM SIMULATIONS

The more simulation becomes an established tool in the design of multi-agent systems, the more urgent the question becomes how valid the induced properties and behavior patterns are. Answering this question depends on the validity of the models, the correctness of the simulators, and the simulations. In all of these aspects, reuse and a declarative representation plays a crucial role. With James II, we developed a customizable framework for modeling and simulation. Its component-based architecture supports a reuse of models, simulators, and experimental settings. The benefits of this architecture for agent-based modeling and simulation will be illuminated by an excerpt of a simulation study about trading strategies for mobile ad hoc networks.     

Multi-agent simulation on multiple GPUs

Multi-agent simulation is widely used in many areas including biology, economic, political, and environmental science to study complex systems. Unfortunately, it is computationally expensive. In this paper, we shall explore the implementation of a general multi-agent simulation system on a system with multiple GPUs acting as accelerators. In particular, we have ported the popular Java multi-agent simulation framework MASON to a nVidia CUDA-based multi-GPU setting. We evaluated our implementation over different numbers and types of nVidia GPUs. For our evaluation, we ported three models in the original version of MASON. On the well-known Boids model, we achieved a speedup of $187\times$. Using a fictional model, we showed that speedup of up to $468\times$ is possible. In the paper, we shall also describe the detailed internals of our system, and the various issues we encountered and how they were solved.     

基于模糊人工势场法的多智能体编队控制及避障方法

针对动态环境中多智能体编队控制及避障问题,提出了一种基于模糊人工势场法的编队方法。首先,在领航跟随法的框架下控制编队队形,在动态队形变换策略的异构模式下,使用人工势场法为多智能体编队中每个智能体规划避障路径;其次,利用模糊控制器控制跟随智能体追踪领航智能体,同时保持跟随智能体之间与领航智能体的相对距离,遇到未知障碍物时,及时保持多智能体编队之间的队形并避免碰撞障碍物。针对人工势场法在引力增量系数和斥力增量系数设置的局限性,利用模糊控制器选择出适应环境的增量系数。Matlab仿真实验结果表明,该方法能够有效地解决复杂环境下多智能体编队控制及避障问题,使用效率函数对实验数据进行分析,验证了所优化方法的合理性和有效性。     

A<Emphasis Type="SmallCaps">rgonauts</Emphasis>: a working system for motivated cooperative agents

This paper presents the Argonauts multi-agent framework which was developed as part of a one year student project at Technische Universität Dortmund. The Argonauts framework builds on a BDI approach to model rational agents that act cooperatively in a dynamic and indeterministically changing environment. However, our agent model extends the traditional BDI approach in several aspects, most notably by incorporating motivation into the agent’s goal selection mechanism. The framework has been applied by the Argonauts team in the 2010 version of the annual multi-agent programming contest organized by Technische Universität Clausthal. In this paper, we present a high-level specification and analysis of the actual system used for solving the given scenario. We do this by applying the GAIA methodology, a high-level and iterative approach to model communication and roles in multi-agent scenarios. We further describe the technical details and insights gained during our participation in the multi-agent programming contest.     

Development of a multi-agent-based distributed simulation platform for semiconductor manufacturing

Since the semiconductor manufacturing system is a large-scale complex system, it is difficult to solve complex problems in semiconductor manufacturing by the mathematical modeling method. This paper presents a multi-agent-based distributed simulation platform to support the extremely complex semiconductor manufacturing analysis. A multi-agent-based distributed simulation platform framework and a multi-agent collaborative control model are proposed to provide a flexible infrastructure and a multi-agent coordination mechanism in distributed environment for semiconductor manufacturing simulation. A multi-agent time synchronization model for distributed simulation is designed to keep events in the correct logical time order in simulation and steps of time synchronization are given. An interaction model and message formats are presented to describe how agents communicate with each other in simulation. The platform development and the design of graphical user interface are also exploited in this paper. Finally, evaluation of this multi-agent-based platform was illustrated with a case study. It indicates that the platform is effective in modeling and simulating the complex semiconductor manufacturing and provides the insights about how to improve the semiconductor manufacturing process with well targeted measures.     

“Virtual laboratory environment” (VLE): a software environment oriented agent and object for modeling and simulation of complex systems

Our research relates to multi-agent and oriented object modeling and simulation of the complex systems. Our research interest itself more particularly with system where the spatial and temporal component make a great part of system to model (for example, ecosystems or systems of production). Within the framework of this article, we will be interested in the flexible production systems.The simulation of complex systems requires generally the integration and the coupling of heterogeneous models (multi-agent, mathematical, and so on). This heterogeneity is a consequence of the diversity of the disciplines and abilities of designers. The approach that we develop consists in the development of “virtual laboratories ”. Our platform “virtual laboratory environment” (VLE) enables us to specify, simulate and analyze spatial complex systems. VLE is based on the concepts of reactive agents, objects and spatial and temporal multi-scale systems.