具有并发延迟功能的PROLOG语言控制策略的设计与实现

本文对一般Prolog语言的控制策略所存在的问题进行了分析,提出了一个新的具有并发延迟功能的控制策略,它有效地避免了递归谓词的低效或无限循环调用,也可实现事实子目标的优化查询,提高了系统的效率、完善了系统的功能。     

基于混合推理的知识库的构建及其应用研究

该文提出了基于OWL本体与Prolog规则的平面几何知识库的构建方法,从而可形式化地表示平面几何中丰富的语义信息.一方面,用类型、定义域、值域、分类、属性、实例等本体描述来表达结构化的知识,为领域内概念与概念之间关系的描述提供形式化的语义;另一方面,用Prolog规则来解决本体不能有效表达的诸如属性之间的关系和操作等问题,从而支持复杂关系间的推理.在此基础上,用Protégé和Prolog构建了一个基于本体和规则的平面几何知识库.实验证明:此知识库可实现知识和语义层次上的信息查询,还可进行复杂问题求解,其丰富的语义描述和混合推理能力弥补了传统知识库的不足.     

逻辑程序综合的一种方法

木文给出逻辑程序综合的一种方法,按规范说明则p(X)Formula(X)中一阶公式Formula(X)的结构,反复运用变换规则,得到一个正确定义谓词p(X)Prolog程序。对于递归说明,也给出了相应的方法。     

基于规则的公交识别专家系统设计与实现

通过对专家系统模型的分析,详细设计了基于规则的公交识别专家系统的构造模块,并根据专家系统的目标创建了事实库和规则库。在规则库的创建中,改进了规则的线性推理机制,提出递归调用规则：利用递归调用形式的规则库实现了两站间直达、N次中转可达路径的查找问题,并实现了两站路径的最小耗费计算。最后通过Visual Prolog 7.3,验证实验结果,得出实验结论。     

基于规则的公交识别专家系统设计与实现

通过对专家系统模型的分析,详细设计了基于规则的公交识别专家系统的构造模块,并根据专家系统的目标创建了事实库和规则库.在规则库的创建中,改进了规则的线性推理机制,提出递归调用规则:利用递归调用形式的规则库实现了两站间直达、N次中转可达路径的查找问题,并实现了两站路径的最小耗费计算.最后通过Visual Prolog 7.3,验证实验结果,得出实验结论.     

Fuzzy Prolog系统XDFPS(I)的研制

我们在Turbo-Prolog系统上加上一个外壳,构成一个Fuzzy Prolog系统XDFPS(I)。用户使用该系统就可在Turbo Prolog上处理模糊信息。本文首先叙述XDFTS(I)的设计思想,而后介绍系统的具体实现以及使用方法,并附上系统的部分程序清单以及系统应用的例子。     

基于Visual Prolog的TSP(货郎担)问题实现

货郎担问题是一个典型的易于描述却难以处理地NP完全问题。而Visual Prolog语言的匹配合一、递归和回溯等特点非常适合求解这类问题。本文利用Visual Prolog实现了简单的货郎担问题。     

基于Prolog语言的商品推荐知识库模型

针对电子商务个性化推荐问题的特点,引入知识工程的树状表示法,将商品推荐中的三要素转化成描述树进行表达,建立基于Prolog语言的个性化推荐知识库模型。该模型可以根据顾客浏览商品先后次序求解,并与数据库动态地交互数据,实现在线购物的个性化营销。该模型相对独立,通过数据库接口共享电子商务系统数据,能够适应不同结构的电子商务系统。     

Prolog在开发智能教学系统中的应用

近几年来,随着计算机与网络技术的迅猛发展,特别是因特网的大规模普及,人工智能的研究活跃起来,并正向更广阔的领域发展。VisualProlog是国际上已经广泛流行的功能强大的通用智能化应用集成开发环境,引进Prolog,对于开发智能应用系统十分必要。论文主要分析了Prolog语言的特点及基本结构,并分析了智能教学系统的工作原理,在此基础上给出了采用Prolog语言实现的一个简单的学生模糊综合评价的专家系统。其中对学生的评估指标分为五级,相应地规定了五条评估规则。     

基于Prolog语言的装备维修决策专家系统设计与开发

采用Prolog智能语言对装备维修决策专家系统进行了设计与开发。从维修知识和推理能力两方面研究了装备维修专家的基本特点;分析了基于Prolog的专家系统的基本语法、推理方式及其在装备维修中的应用方式;设计并开发了装备维修决策专家系统的元知识库、关联知识库和应用知识库;最后采用Visual Prolog和VC++混合编程方式开发了某装备维修决策专家系统。     

描述性语言与知识库系统

1．引言 在数据库领域,由于描述性语言SQL的成功使用,描述性更加受到重视[1,2,3,4]。描述性语言Data-log的提出,使得一些学者认为知识库语言应该是描述性的程序设计语言[4]。然而多年来的研究,不但没有开发出一个描述性的知识库程序设计语言,而且还阻碍了知识库的实用化[5]。本文研究了描述性与逻辑语言、知识库语言之间的关系,提出了三个观点：1)描述性语言是计算不完备的,因此不能作为独立的程序设计语言；2)逻辑语言是描述性语言的合适形式；3)追求语言的描述性是知识库系统实用化的障碍之一。     

专家系统的实时性分析

在一些复杂控制系统中 ,专家系统作为一个决策控制器 ,满足整个系统的性能需要 ,因此研究专家系统的推理时间是必要的。专家系统的推理时间与其推理模式、知识库结构等因素有关。针对知识库结构 ,通过专家系统的推理时间研究了实时性问题 ,利用时间齐次马尔可夫链为专家系统知识库进行建模 ,并给出了相应的时间估计模型及其排列准则。     

知识库查询的固有低效性

1．引言 知识库研究的重要内容之一是查询优化技术的研究。从七十年代后期开始,经过八十年代,到目前为止,知识库查询优化方面已经取得了大量研究成果,开发了许多有效的查询优化算法。例如：Semi-Naive求值[1]、Magic-Set重写技术和Counting方法[2,3]等等。这些技术的应用,大大提高了知识库的查询效率。尽管如此,知识库的查询效率仍不能令人满意,这是知识库系统不能实用的主要原因之一。     

Intelligent Jig System to Automate Flexible Manufacturing System

Jig pallet systems are intended for the automatic, complete machining or assembly of parts families in the area of medium and large scale manufacturing. Their distinctive feature is that several machine tools, or assembly machines, are linked together to generate an overall system by means of common tool and workpiece supply with integrated computer control. A jig pallet system is considered to be intelligent, if its central processor is equipped with a knowledge-base and an inference-engine. A jig pallet system was structured. It consists of a central processor, tools supply system, workpiece supply system, manufacturing cell which includes four work stations and a local area network. A knowledge-base and inference-engine were developed to reason the next position of jig pallet systems. The jig pallet system, with its incorporated knowledge-base and inference-engine, was tested for a large variety of operational parameters to explore the ability of the central processor to control participant's operations. The conclusion which is derived from these tests is that the central processor can control and optimize participant's operation in real time with minor effects on the system efficiency.     

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。     

An Inheritance System with Regular Expressions and IS-A Relation

In this paper, we propose an inheritance system for knowledge-bases in which IS-A relation and IS-NOT-A relation are specified on the domain extended by dot notation .. Due to the simplicity of the framework, we can obtain several computational advantages including the following: (1) IS-A relation and IS-NOT-A relation are determined in polynomial time. (2) Satisfiability of a given knowledge-base is also determined in polynomial time. (3) Set-at-a-time queries are completely answered by regular expressions. (4) Regular expressions are also used for specifications of knowledge-bases. Consequently, we can achieve advanced reasoning by the computational operations on regular sets using union, intersection, and difference. Furthermore, the obtained results can be incrementally reused to specify new knowledge-bases. Several applications of the proposed inheritance reasoning mechanism in advanced computer systems are also demonstrated.     

Verification and validation of Bayesian knowledge-bases

Knowledge-base V&V primarily addresses the question: “Does my knowledge-base contain the right answer and can I arrive at it?” One of the main goals of our work is to properly encapsulate the knowledge representation and allow the expert to work with manageable-sized chunks of the knowledge-base. This work develops a new methodology for the verification and validation of Bayesian knowledge-bases that assists in constructing and testing such knowledge-bases. Assistance takes the form of ensuring that the knowledge is syntactically correct, correcting “imperfect” knowledge, and also identifying when the current knowledge-base is insufficient as well as suggesting ways to resolve this insufficiency. The basis of our approach is the use of probabilistic network models of knowledge. This provides a framework for formally defining and working on the problems of uncertainty in the knowledge-base.

In this paper, we examine the project which is concerned with assisting a human expert to build knowledge-based systems under uncertainty. We focus on how verification and validation are currently achieved in .     

Learning-enhanced adaptive DSS: a Design Science perspective

The process of acquiring, maintaining, updating, and using appropriate domain-specific knowledge has played an integral role in knowledge-based decision support systems. Although each of these stages is necessary and important, knowledge-based systems that operate in dynamic environments can become quickly stale when core knowledge embedded in these systems are not continually updated to reflect changes in the system over time. Clearly, stale knowledge could be faulty and cannot be relied upon for making decisions and a knowledge-based decision support system with stale knowledge may even be detrimental in the long run. We consider a generic adaptive DSS framework with learning capabilities that continually monitors itself for possible deficit in the knowledge-base, expired or stale knowledge already present in the knowledge-base, and availability of new knowledge from the environment. The knowledge-base is updated through incremental learning. We illustrate the generic knowledge-based adaptive DSS framework using examples from three different application areas. The framework is flexible in being able to be modified or extended to accommodate the idiosyncrasies of the application of interest. The framework considered is an example artifact that naturally satisfies the Design Science perspective.