基于SELD描述语言的英文科技文本知识自动获取

知识获取一直是智能系统的瓶颈问题.本文将介绍我们设计的一种英文科技文献描述语言--SELD以及基于SELD的文本知识自动获取技术.SELD是一种与科技英语十分相近的类自然语言,英文科技资料经SELD语言稍加改写后,基于SELD的知识获取系统SELKAS便可以自动对其进行编译、知识提取和整理,形成领域知识库.SELKAS可作为智能系统建造过程中的知识自动获取工具.     

网络结构挖掘算法研究

网络结构挖掘是以超链接分析为基础,从链接结构中获取有用的知识,利用这些知识,重新组织结构,使内容逻辑结构更加合理.深入研究现有的网络结构挖掘系统,并在对其核心算法PageRank和HITS中所存在的问题作了详细分析的基础上提出了自己的改进算法,主要是对每个网页定义这三个参数:PageRank,Authority,Hub,并进行分析与优化,以便得到更好的查询结果,最后设计了一个改进网络结构挖掘系统原型,根据实验结果进行分析.     

人工智能的应用及困惑

知识获取、知识表示和知识利用成为人工智能的中心问题.但在当前的智能系统中,他们都极不完善.本文通过对人工智能的四大应用领域:专家系统、智能教学系统、自然语言理解及自动程序设计进行分析,说明了在人工智能的应用领域中困难重重.     

因特网知识分布研究综述

因特网知识的自动获取一直是智能系统的瓶颈。而因特网网页的分布形态、网页知识的分布又在一定程度上影响知识获取及其应用技术。该文就因特网的网页分布、网页间的链接形态进行综述，指出其中存在的问题，并以此为基础，介绍了用随机行走的方法来研究因特网知识的分布。     

分布式问题求解在CIMS中的应用

本文介绍了一个应用于 CIMS 的分布式智能系统,该系统将 CAD,CAPP,CAM 有机地集成为一体.由于采用了分布式问题求解技术、通讯技术,使在不同地理位置上的计算机性能和资源得以充分发挥和利用.多个处理结点组成的基于黑板结构的分布式智能系统,可以高速、并行地执行分散在不同结点的知识源,提高系统的灵活性和处理能力.运行在三个结点(可扩充多个)的系统大体相同.主要包括:知识获取子系统、前端处理(负责接收与发送)、任务的分解、任务的评估与分配、任务的综合、后端处理(子问题求解)模块以及解释子系统.目前该系统已在由以太网连接的 SUN 计算机上运行,分布式智能系统本身由 C 语言写成,被集成的子系统由 C 和 FORTRAN 两种语言写成.     

基于遗传算法的Fuzzy规则自动获取的研究

为了实现Fuzzy规则自动获取,进而构造高性能智能系统和解决智能系统的瓶颈问题,研究了利用遗传算法自动获取规则的方法以及遗传算法的组合优化能力.模拟结果表明,这是一种有效地获取Fuzzy规则的方法.     

论机器学习

随着计算机和网络技术的高速发展,特别是在这个大数据时代,人们对计算机系统的要求越来越高。以往大部分智能计算机系统都普遍缺少自我学习的功能,如遇到错误时不能自动校正;不会通过经验改进自身的功能;不会自动获取所需的知识。一个不具有学习能力的智能系统算不上是真正的智能系统,所以机器学习在现今是一项十分重要的技术。     

用XML建造专家系统知识库

专家系统是一种基于知识的智能系统,主要包括知识库、数据库、推理机制、解释机制、人机接口和知识获取等功能模块。它的主要特征是,系统中有一个巨大的知识库,存贮某个领域的专门知识,有很多种技术管理知识库。随着网络技术的发展,XML已经成为重要的网络计算语言。文中就如何用XML建造专家系统的知识库以及其相关操作做了深入的探讨。     

用XML建造专家系统知识库

专家系统是一种基于知识的智能系统，主要包括知识库、数据库、推理机制、解释机制、人机接口和知识获取等功能模块。它的主要特征是，系统中有一个巨大的知识库，存贮某个领域的专门知识，有很多种技术管理知识库。随着网络技术的发展，XML已经成为重要的网络计算语言。文中就如何用XML建造专家系统的知识库以及其相关操作做了深入的探讨。     

可视化智能界面系统研究

可视化自适应人机交互界面的设计是智能系统能否成功实施与应用的关键之一，基于此提出了将Agent技术引入用户界面设计中。界面采用可视化的知识表示，知识库直观，知识获取直接面对领域专家，实现了知识自动获取；同时界面针对不同用户群设计了多种界面变体，从而增强了系统的灵活性和对用户的适应性；建立了智能引导和帮助，集成了语音合成技术，实现了界面系统的“声形”并茂，降低和减少了用户使用系统的要求与学习时间，使系统更易学易用。     

专家控制系统的知识库维护

专家系绕的知识获取和表示一向是专家系统开发的“瓶颈”,而知识库的维护在大型专家系统中所占的地位更是不可忽视,实时专家系统作为专家系统的一个颇有前景的研究方向,对知识库的维护又提出了哪些新的要求呢?本文试图通过减压精馏过程智能多变量控制系统(DIMCS)~*的知识库维护,探索实时专家系统中从知识表示、知识库组织到一致性维护等一系列技术的实现方式。     

Learning task models in ill-defined domain using an hybrid knowledge discovery framework

Domain experts should provide Intelligent Tutoring Systems (ITS) with relevant domain knowledge that enable it to guide the learner during problem-solving learning activities. However, for ill-defined domains this knowledge is hard to define explicitly. Our hypothesis is that knowledge discovery (KD) techniques can be used to extract problem-solving task models from the recorded usage of expert, intermediate and novice learners. This paper proposes a procedural-knowledge acquisition framework based on a combination of sequential pattern mining and association rules discovery techniques. The framework has been implemented and is used to discover new meta-knowledge and rules in a given domain which then extend domain knowledge and serve as problem space, allowing the Intelligent Tutoring System to guide learners in problem-solving situations. Preliminary experiments have been conducted using the framework as an alternative to a path-planning problem solver in CanadarmTutor.     

电能质量数据采集策略的研究与实现

电能质量数据采集主要指PQDIF（Power Quality Data Interchange Format）文件的采集,通过对PQDIF文件的来源以及电能质量智能信息系统数据需求的研究,给出一种高效可靠的数据采集策略。该策略对PQDIF文件的采集主要包括自动和手动两种方式。通过对传统多线程FTP技术的改进,可以实现采集特定时间段内创建的PQDIF文件,为电能质量智能信息系统的运行提供实时可靠的基础数据来源。     

An Adaptable IE System to New Domains

The most extended way of acquiring information for knowledge based systems is to do it manually. However, the high cost of this approach and the availability of alternative Knowledge Sources has lead to an increasing use of automatic acquisition approaches. In this paper we present M-TURBIO, a Text-Based Intelligent System (TBIS) that extracts information contained in restricted-domain documents. The system acquires part of its knowledge about the structure of the documents and the way the information is presented (i.e., syntactic-semantic rules) from a training set of these. Then, a database is created by means of applying these syntactic-semantic rules to extract the information contained in the whole document.     

A flowchart‐based intelligent tutoring system for improving problem‐solving skills of novice programmers

Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem‐solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one‐to‐one personalized and adaptive teaching. In this paper, a novel Flowchart‐based Intelligent Tutoring System (FITS) is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem‐solving activities and learning computer programming. FITS not only takes full advantage of Bayesian networks, but also benefits from a multi‐agent system using an automatic text‐to‐flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem‐solving skills. In the end, in order to investigate the efficacy of FITS in problem‐solving ability acquisition, a quasi‐experimental design was adopted by this research. According to the results, students in the FITS group experienced better improvement in their problem‐solving abilities than those in the control group. Moreover, with regard to the improvement of a user's problem‐solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge.     

一种增强型智能决策支持系统的结构设计

本文描述了ＩＤＳＳ的基本概念、结构及其特征，提出了一种增强型智能决策支持系统（ＥＩＤＳＳ）的基本结构框架，在ＩＤＳＳ结构中引入中心控制器、知识获取系统、学习系统、推理机，从而大大增强了ＩＤＳＳ的功能。本文还对ＥＩＤＳＳ中各组成部分的功能及实现方法作了讨论。     

Using a hybrid rule-based approach in developing an intelligent tutoring system with knowledge acquisition and update capabilities

In this paper, we present the architecture and describe the functionality of an Intelligent Tutoring System (ITS), which uses an expert system to make decisions during the teaching process. The expert system uses neurules for knowledge representation of the pedagogical knowledge. Neurules are a type of hybrid rules integrating symbolic rules with neurocomputing. The expert system consists of three components: the user modelling unit, the pedagogical unit and the inference system. The pedagogical knowledge is distributed in a number of neurule bases within the user modelling and the pedagogical unit. Another important component of the ITS, for both its development and maintenance, is its knowledge management unit, which provides knowledge acquisition and knowledge update capabilities to the system, that is, offers expert knowledge authoring capabilities to the system.     

一种协同式智能决策支持系统的研究与实现

智能决策支持系统（IDSS）是决策支持系统（DSS）的重要研究方向，本文将专家系统知识看作IDSS知识的组成要素，在给出异构知识一描述的基础上，研究了协同式IDSS的协同机制，提出了一种协同式IDSS（CIDSS）模型。     

ICADS: Intelligent Car Audio Design System for product planning

This paper describes the development of a system, ICADS (Intelligent Car Audio Design System), that can be used to effectively support product development engineers in car audio product design. We have demonstrated the use of expert system technology and the technique of quality function deployment (QFD) in supporting car audio design planning. QFD is applied as a knowledge acquisition method in this study to support car audio design teams in the development of products in a structured way that relates market demand via engineering specifications to parts specifications. We believe that other industries can use a similar approach for developing an expert system that can support product design planning.     

Student diagnosis in practice; bridging a gap

This paper presents a novel framework for looking at the problem of diagnosing a student's knowledge in an Intelligent Tutoring System. It is indicated that the input and the conceptualisation of the student model are significant for the choice of modeling technique. The framework regards student diagnosis as the process of bridging the gap between the student's input to the tutoring system, and the system's conception and representation of correct knowledge. The process of bridging the gap can be subdivided into three phases, data acquisition, transformation and evaluation, which are studied further. A number of published student modeling techniques are studied with respect to how they bridge the gap.