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基于广义细胞自动机的网络信息自组织利用方法
引用本文:帅典勋,刘燕.基于广义细胞自动机的网络信息自组织利用方法[J].计算机学报,2003,26(8):897-905.
作者姓名:帅典勋  刘燕
作者单位:清华大学智能技术与系统国家重点实验室,北京,100080;华东理工大学计算机科学与工程系,上海,200237
基金项目:国家自然科学基金重点项目 (60 13 5 0 10 ),国家“九七三”重点基础研究发展规划项目 (G19990 3 2 70 7),国家自然科学基金 (60 0 73 0 0 8),清华大学智能技术和系统国家重点实验室开放课题基金资助
摘    要:目前的网络信息利用模式存在着严重缺陷,它将网络上发生的海量、随机、分布、并行的信息利用行为当作是没有后效的和彼此无关的.该文提出一种新的基于网络信息自组织的信息利用模式以及基于广义细胞自动机的网络信息自组织方法.按照本文的信息利用模式,网络信息利用行为总是伴随着信息内容在网络中的扩散,网络信息利用行为成为有后效的和相关的,从而导致不同信息内容和不同内容粒度的分布式的信息自组织结构,形成基于这种信息自组织结构的网络信息利用模式.文中进而提出一种广义细胞自动机的模型、结构和算法,通过群体智能,发现网络中的信息自组织结构.分析和实验表明,基于广义细胞自动机的的网络信息自组织利用模式,在效率、自适应性和可靠性等方面优于目前的网络信息利用方法.

关 键 词:计算机网络  网络信息自组织  广义细胞自动机  信息利用模式  细胞神经网络
修稿时间:2002年6月8日

A Novel Self-Organizing Approach to Network Information Exploitation Based on Generalized Cellular Automata
SHUAI Dian-Xun,LIU Yan.A Novel Self-Organizing Approach to Network Information Exploitation Based on Generalized Cellular Automata[J].Chinese Journal of Computers,2003,26(8):897-905.
Authors:SHUAI Dian-Xun  LIU Yan
Abstract:The present mode of network information exploitation has some serious drawbacks because that the massive, stochastic, distributed and parallel behaviors related to information exploitation in the networks are always considered to be of non-aftereffect and independence from each other. This paper presents a novel self-organizing mode of network information exploitation, and a generalized cellular automata (GCA) approach to the network information self-organization. By our methodology, every information exploitation behavior happened in networks is always associated with proliferating some information contents among the networks to a certain extent, so that some self-organized distributed patterns in terms of different information contents and different content granularities will be generated by a great number of radom information proliferations in the networks. It is these information self-organizing patterns that our new mode for network information exploitation is based on. This paper is also addressed to the architecture, cellular dynamics and algorithm of a GCA which is used for discovering the above information self-organizing patterns caused by massive exploitation behaviors in the networks. The analysis and simulation have shown many advantages of our approach over presently used methods in respect to efficiency, robustness, suitability and reliability for network information exploitation.
Keywords:network information exploitation  generalized cellular automata  swarm intelligence  self-organizing pattern
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