DPMAS:一种多主体系统设计方法

提出一种多主体系统设计方法DPMAS。首先,该方法以对象作为主体实现的基础,以UML作为基本的建模语言,保证了与面向对象方法的延续性。同时,该方法提供了新的图来表示多主体系统中的知识模型和主体间交互模型,并对UML符号进行了一些改进以表示主体特有的概念。     

Multi-agent segmentation of IVUS images

A novel multi-agent image interpretation system has been developed which is markedly different from previous approaches in especially its elaborate high-level knowledge-based control over low-level image segmentation algorithms. Agents dynamically adapt segmentation algorithms based on knowledge about global constraints, contextual knowledge, local image information and personal beliefs. Generally agent control allows the underlying segmentation algorithms to be simpler and be applied to a wider range of problems with a higher reliability.The agent knowledge model is general and modular to support easy construction and addition of agents to any image processing task. Each agent in the system is further responsible for one type of high-level object and cooperates with other agents to come to a consistent overall image interpretation. Cooperation involves communicating hypotheses and resolving conflicts between the interpretations of individual agents.The system has been applied to IntraVascular UltraSound (IVUS) images which are segmented by five agents, specialized in lumen, vessel, calcified-plaque, shadow and sidebranch detection. IVUS image sequences from 7 patients were processed and vessel and lumen contours were detected fully automatically. These were compared with expert-corrected semiautomatically detected contours. Results show good correlations between agents and expert with r=0.84 for the lumen and r=0.92 for the vessel cross-sectional areas, respectively.     

Distributed intelligence for multi-camera visual surveillance

Latest advances in hardware technology and state of the art of computer vision and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. The paper proposes a multi-agent architecture for the understanding of scene dynamics merging the information streamed by multiple cameras. A typical application would be the monitoring of a secure site, or any visual surveillance application deploying a network of cameras. Modular software (the agents) within such architecture controls the different components of the system and incrementally builds a model of the scene by merging the information gathered over extended periods of time. The role of distributed artificial intelligence composed of separate and autonomous modules is justified by the need for scalable designs capable of co-operating to infer an optimal interpretation of the scene. Decentralizing intelligence means creating more robust and reliable sources of interpretation, but also allows easy maintenance and updating of the system. Results are presented to support the choice of a distributed architecture, and to prove that scene interpretation can be incrementally and efficiently built by modular software.     

多智能体协作的两层强化学习实现方法

提出了多智能体协作的两层强化学习方法。该方法主要通过在单个智能体中构筑两层强化学习单元来实现,将该方法应用于3个智能体协作抬起圆形物体的计算机模拟中,结果表明比采用传统强化学习方法的智能体协作得更好。     

多Agent系统中依赖关系的研究

多Ａｇｅｎｔ系统是分布式人工智能研究的一个重要分支。本文详细讨论了多Ａｇｅｎｔ系统中死锁依赖的性质以及三种解决死锁依赖的方法,为多Ａｇｅｎｔ系统中稳定性和活性的研究提供了有效的途径。     

多智能体时滞网络的加权平均一致性

提出了线性和非线性分布式协调控制器,使多智能体系统取得加权平均一致性.对于线性控制器,考虑网络存在通信时间延迟的情形,给出了能容忍的最大固定时滞的一个紧凑上界.考虑网络结点控制输入的有界约束,给出了一类非线性分布式控制器的收敛性分析.结果说明,网络的连通性是系统取得一致性的关键.最后对时滞网络的情形给出了仿真示例.     

利用方位角信息的移动机器人编队控制

本文以移动机器人为研究对象,仅利用方位角信息实现多智能体系统的编队控制.为实现大规模编队和队形的缩放控制,智能体被分为领航者、第1跟随者以及其余跟随者.首先,考虑智能体之间相对位置信息难以精确测量的情形,设计仅用方位角信息的估计算法获得准确的相对位置;然后,基于获得的相对位置信息设计第1跟随者的控制算法,使得第一跟随者...     

面向装配的多Agent协同设计研究

分析了面向装配设计（ＤＦＡ）技术发展和存在的问题,提出了ＤＦＡ中多Ａｇｅｎｔ协同设计研究的必要性。对设计Ａｇｅｎｔ的概念、结构及设计过程进行了描述和研究,并在此基础上对多Ａｇｅｎｔ协同设计过程和表示做了进一步的研究。最后,对面向ＤＦＡ的多Ａｇｅｎｔ协同原型设计系统进行了设计。     

MAS技术在生产调度研究中的应用

对MAS技术在生产调度领域中的研究成果进行分析，归纳出MASS的基本特征和系统框架，按照不同的技术路线将MASS分成实体型MASS和过程型MASS两大类，并对两大类MASS的代理定义，系统结构和主要代理协商策略等进行具体分析，同时对两类系统的特点做了比较，最后展望了MAS研究的未来发展趋势。