基于Multi-Agent的数据挖掘模型的研究

数据挖掘是人们长期对数据库研究的结果，但是传统的数据挖掘存在低效性和非智能化等不足。随着具有自主性和社会性的智能计算实体Agent的出现和发展，文中将Multi-agent技术应用到数据挖掘中，并提出了基于Multi-agent智能化的数据挖掘模型，讨论了模型的运行过程。这一模型弥补了传统数据挖掘的缺陷和不足，而且在很大程度上提高了数据挖掘的智能性和高效性，减少了人工的参与。     

基于Agent技术的数据挖掘模型研究

传统的数据挖掘方法存在效率低和非智能化等不足,难以满足网络环境下对海量数据的挖掘需要。文中从数据挖掘技术与Agent技术的特征出发,论述了两者结合的优势,将Agent技术应用到数据挖掘中,提出了基于Agent的数据挖掘模型,并阐述了该模型的组织结构。该模型能够降低问题的复杂性,减少人工的参与,在很大程度上提高了数据挖掘的智能性和高效性。     

基于Agent技术的数据挖掘模型研究

传统的数据挖掘方法存在效率低和非智能化等不足,难以满足网络环境下对海量数据的挖掘需要。文中从数据挖掘技术与Agent技术的特征出发,论述了两者结合的优势,将Agent技术应用到数据挖掘中,提出了基于Agent的数据挖掘模型,并阐述了该模型的组织结构。该模型能够降低问题的复杂性,减少人工的参与,在很大程度上提高了数据挖掘的智能性和高效性。     

基于Multi-agent技术的知识发现新模型KDD的设计

KDD模型是基于双库协同机制的知识发现新模型,是结构化数据挖掘领域研究的一个新的分支。为了进一步提高KDD的智能性,文章设计了一个基于Multi-agent技术的智能数据挖掘系统。利用多智能体技术,实现了数据预处理、数据挖掘、知识的自动获取、基础数据库与知识库的同步进化与协调、知识的评价与表示等功能,为智能信息系统的发展提供了重要支持。     

基于数据立方体的联机分析挖掘模型研究

本文提出一种联机分析挖掘模型,该模型基于数据仓库和其他各种类型文件生成的工作仓库,同时综合了联机分析处理多维分析的在线性、灵活性和数据挖掘处理的智能化特点,提高了传统模型的灵活性和智能化程度。     

基于数据挖掘的智能化学习系统DMBILS

在论述数据仓库和数据挖掘的基本概念和关键技术的基础上,介绍了数据挖掘在网络教育中,特别是在智能化学习系统中的应用。在同时考虑智能化学习需要和数据挖掘需要的前提下,提出了一个基于数据挖掘的三层智能化学习系统(DMBILS,DM-basedintelligentlearningsystem)应用模型。     

企业智能化Web商务数据挖掘引擎算法设计与实现

针对现有的数据挖掘引擎难以满足企业不断增长的需求的问题,在传统搜索引擎的基础上提出了一种企业专用的智能化Web商务数据挖掘引擎.从总体上给出了模型框架,得出企业智能化Web商务数据挖掘引擎是一个基于多模型的系统的结论.接着分别详细研究了该引擎的:Web内容挖掘、Web信息获取、客户行为挖掘3个核心模块.并给出了3个模块的详细算法设计和实现过程     

基于Multi-agent的选课系统模型设计

选课系统是当代学校信息化教务管理的重要组成部分,但是传统系统有许多缺点和不足之处。该文在简单介绍了选课工作流程之后,用Multi-agent技术和思想设计了这个系统,提出了基于Multi-agent的选课系统的模型框架和结构设计,大大地提高了当代学校教务管理系统的高效性、智能性和高度集成,促进学校的信息化建设。     

基于Multi-agent的垃圾邮件检测系统的研究与实现

本文在简单介绍了垃圾邮件检测的现有技术、支持向量机和Multi-agent技术的基础上,新提出一个分布式垃圾邮件检测系统MAUS(Multi-agent Anti-UCE system),该系统将分布式应用中的Multi-agent技术与垃圾邮件检测技术很好地结合在一起.本文具体讲述了基于Multi-agent的分布式垃圾邮件检测系统模型的体系结构、关键技术和在Window平台上使用DCOM技术的实现方法.实现结果说明该系统模型具有良好的实用价值.     

一种面向遥操作的多指手Multi-agent模型结构

提出一种面向遥操作的多指手Multi-agent模型逻辑结构,采用Java语言设计了用户Agent、管理Agent 和手指Agent模型以及Multi-agent之间的通讯模块．使用Java3D与VRML建立多指手三维虚拟模型,开发了网络遥操作仿真平台．最后,在该平台上实现了多指手遥操作的虚拟再现,验证了多指手Multi-agent模型的可行性．     

Key Technologies of Confrontational Intelligent Decision Support for Multi-Agent Systems

This paper firstly studies intelligent learning techniques based on reinforcement learning theory. It proposes an improved multi-agent cooperative learning method that can be shared through continuous learning and the strategies of individual agents to achieve the integration of multi-agent strategy and learning in order to improve the capabilities of intelligent multi-agent systems. Secondly, according to the analysis of data mining and AHP theory, a new concept is proposed to build a data mining model (based on intelligent learning) that has been named ‘ACMC’ (AHP Construct Mining Component); designed ACMC strategy evaluation and assistant decision-making based on multiagent systems, to achieve a strategic assessment of the current situation and reach a final decision. Finally, after research on Intelligent Decision Technology based on game theory, aspects of game theory are employed to deal with the real demand of confrontational environments.     

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.     

用智能MAS实现个性化VR推荐算法研究

通过对虚拟现实和智能多Agent技术的研究,提出了基于智能多Agent系统的虚拟现实技术模型.并在此模型基础上实现了个性化VR推荐算法的研究,从而解决了灵境中虚拟人的智能实现、通信机制等问题,为今后进行远程教学、虚拟实验、仿真研究奠定了理论基础.     

基于MAS的智能决策支持系统研究

为了使多Agent系统的研究和日益强大的网络技术为建立人机一体化的智能决策支持系统提供新的途径和方法，文中在分析了多Agent系统(MAS)体系结构的基础上，对智能决策支持系统(IDSS)进行了探讨，提出一种在网络环境下，基于MAS的智能决策支持系统模型，并给出了其决策支持过程描述、形式化定义。该模型能够为智能决策支持系统提供新的途径和方法，不仅丰富了智能决策理论，而且能够对实际决策提供有效的支持。     

Stereo Video Surveillance Multi-agent System: New Solutions for Human Motion Analysis

This article presents a distributed agent-based system that can process the visual information obtained by stereoscopic cameras. The system is embedded within a global project whose objective is to develop an intelligent environment for location and identification within dependent environments that merges with other types of technologies. In this kind of environments, vision algorithms are very costly and require a lot of time to produce a response, which is highly inconvenient since many applications can require action to be taken in real time. A multi-agent system (MAS) can automate the process of analyzing images obtained by cameras, and optimize the procedure. This study presents a MAS that can process stereoscopic images to detect and classify people by combining a series of novel techniques. The article shows in detail the combination of techniques used to perform the detection process. The process can be subdivided into human detection, human tracking, and human behavior understanding. With the addition of a case-based reasoning (CBR) model, the system can also incorporate reasoning capabilities. The system was tested under different conditions and environments.     

Ontology-based computational intelligent multi-agent and its application to CMMI assessment

This study presents an ontology-based computational intelligent multi-agent system for Capability Maturity Model Integration (CMMI) assessment. An ontology model is developed to represent the CMMI domain knowledge that will be adopted by the computational intelligent multi-agent. The CMMI ontology is predefined by domain experts, and created by the ontology generating system. The computational intelligent multi-agent comprises a natural language processing agent, an ontological reasoning agent and a summary agent. The multi-agent deals with the evaluation reports from the natural language processing agent, infers the term relation strength between the ontology and the evaluation report, and then summarizes the main sentences of the evaluation report. The summary reports are meanwhile transmitted back to the domain expert, which makes the domain expert further adjust the CMMI ontology. Experimental results indicate that the ontology-based computational intelligent multi-agent can effectively summarize the evaluation reports for the CMMI assessment.     

A Reusable Multi-Agent Architecture for Active Intelligent Websites

In this paper a reusable multi-agent architecture for intelligent Websites is presented and illustrated for an electronic department store. The architecture has been designed and implemented using the compositional design method for multi-agent systems DESIRE. The agents within this architecture are based on a generic information broker agent model. It is shown how the architecture can be exploited to design an intelligent Website for insurance, developed in co-operation with the software company Ordina Utopics and an insurance company.     

Dynamic scheduling for multi-site companies: a decisional approach based on reinforcement multi-agent learning

In recent years, most companies have resorted to multi-site or supply-chain organization in order to improve their competitiveness and adapt to existing real conditions. In this article, a model for adaptive scheduling in multi-site companies is proposed. To do this, a multi-agent approach is adopted in which intelligent agents have reactive learning capabilities based on reinforcement learning. This reactive learning technique allows the agents to make accurate short-term decisions and to adapt these decisions to environmental fluctuations. The proposed model is implemented on a 3-tier architecture that ensures the security of the data exchanged between the various company sites. The proposed approach is compared to a genetic algorithm and a mixed integer linear program algorithm to prove its feasibility and especially, its reactivity. Experimentations on a real case study demonstrate the applicability and the effectiveness of the model in terms of both optimality and reactivity.     

基于多Agent的高校教学评测系统模型研究

基于多Agent技术所构造的协同设计系统具有多Agent系统的分布性、协作性和智能性的特点。在分析目前高校教师教学评价系统的基础上,提出了一个基于多Agent系统(MAS)的高校教师教学评测系统模型(MATES),并着重讨论了该系统中各Agent的功能和Agent之间的协作关系。该模型为教师教学评价的自动化、智能化和高效率提供了一个良好的计算平台。