摩托车智能设计中知识库系统的研究与实现*

根据摩托车设计知识的特点和分类,应用面向对象的思想实现了实例知识的表示并构建了层次实例库,采用CLIPS语言实现规则知识的表示并创建了规则库。同时运用了元知识的思想实现各个子知识库之间的管理和知识的利用,建立了知识库管理系统,有效地实现了摩托车设计经验知识的获取、查询和维护,为摩托车智能设计提供了知识支持。     

基于人工智能的知识库应用模型构建方案的研究

结合人工智能在数据库中的应用实践,在对人工智能在数据库中应用前景、设计思想、理论基础等论述的基础上,得出了知识库的概念,进而讨论了几种知识库应用模型的构建方法。     

基于Ontology的平面几何知识库设计

文中给出了Ontology建模方法在平面几何知识库设计中的一个应用实例，先从总体上介绍了Ontology建模方法的思想，框架结构及优点，再详细描述了平面几何知识库的设计、组织结构及其实现，我们提出概念分层，等价类存贮、启发式双向推理、推理经验生成，交互解题，数字测试等多种平面几何知识库的相关技术，能生成与“吴法”等代数方法不同的、传统的、较优的可读证明，并提高了系统的重用性、可靠性、可维护性和知识扩展性。     

基于关系数据库的智能教学系统知识库设计

本文通过对知识库和关系数据库的比较,说明了基于关系数据库设计知识库的可行性,并以智能教学系统中知识库的构建过程为例,介绍了知识表示方法,具体给出了基于关系数据库建立知识库的方法,并简要说明了知识库管理和知识获取方法。     

工艺知识管理系统的设计

工艺知识库是CAPP系统的重要组成部分。本文提出一种分类层次模型以组织和管理工艺知识，并据此建立工艺知识库PPKBS。用四种形式表达工艺知识，并以外部和内部两种形式表现。提出了工艺知识的冗余、矛盾和句法结构检查方法。     

汉语自动分词专家系统的设计与实现

本文介绍了自动分词专家系统的设计思想和系统结构，并给出了自动分词专家系统知识库的组织与实现方法，以及推理机制的建立和自动分词过程。     

工艺知识管理系统的设计

工艺知识库是ＣＡＰＰ系统的重要组成部分。本文提出了一种分类层次模型以组织和管理工艺知识，并据此建立工艺知识库ＰＰＫＢＳ。用四种形式表达工艺知识，并以外部和内部两种形式表现。提出了工艺知识的冗余、矛盾和句法结构检查方法。     

基于客服系统知识库的设计与实现

本文以客服中心为背景,将知识库引入客服中心的建设与运营中,表达了建设客服中心知识库必要性的思想,并详细介绍了客服中心知识库的设计与实现,具有现实意义。     

基于关系模式的知识表示方法

本文提出了一种专家数据库系统中知识库的设计方法。采用关系模式来描述知识库，用数据库方法存储管理知识库，以解决紧密耦合结构的专家数据库系统的设计问题，改善知识库管理的功能，克服专家系统不能有效地利用现有的使用常规数据库技术建造起来的数据库的局限。     

通用法规知识库系统的设计

建立法规知识库系统有助于增强执法公正性,提高执法效率。文章讨论了法规知识库系统的设计思想和系统结构。系统采用广泛使用的产生式系统实现,并对基本的产生式系统作了一些改进,知识库由规则库和已定案例库组成,推理采用二级推理。最后给出了系统存在的问题及解决的方法。     

Decision support for conceptual bridge design

A knowledge based, decision support tool for the conceptual design of bridges is described. The system incorporates a restructured version of a knowledge base (KB) for conceptual bridge design. The restructuring results in a KB which can be readily altered and extended by system users who are not expert knowledge engineers. This is achieved by using a novel form of KB in which the knowledge is fragmented into separate concepts associated with design solutions. The system also includes a new style of user interface which provides a critiquing style of interaction with the KB only interacting with the user when it detects a possible error or a more suitable design solution. The new system has been developed using object oriented programming techniques which result in a structured and robust style of KB.     

Enhancing students’ knowledge building through utilising social interactions in an online learning environment

ABSTRACT

In the new “open world” of information, educational systems should involve students in constructing new knowledge of value to a community out of fragmentary information. The already proposed Knowledge Building (KB) approaches typically support only a few general-purpose activities due to the constraints of the utilised web-based environments. To organise and facilitate students’ KB during course activities, this study incorporated services provided by DoosMooc social learning environment into a knowledge transformation model. This approach is completely adapted to an educational context and allows time for iterations, helping students to both contribute to social KB processes and take collective responsibility for improving their understanding of authentic problems. The features provided by the introduced environment support and assess students’ KB activities and facilitate processes of creating, representing, organising, and reviewing different types of knowledge artefacts. The results of a semester-long experiment indicate that the approach and the corresponding instructional design thereof could successfully organise students’ KB activities and facilitate the required interactions. This study reports the impacts of parameters such as learner expertise and quality of shared knowledge on the planned KB processes, and investigates the relationships between students' KB activities and learning achievements.     

RENOIR: An expert system using fuzzy logic for rheumatology diagnosis

A new expert system (ES) to aid the nonspecialist physician in diagnosing arthritis and collagen diseases has been developed. Here we present the structure of RENOIR and the results of its implementation. This rule-based ES has been programmed using the MILORD environment. This is a shell to develop ES using a closed set of linguistic labels to express uncertainty. A feature of RENOIR is its five levels of knowledge representation, which permits to build a very flexible knowledge base (KB) and express knowledge with high accuracy. Those rules directed to similar goals are grouped in modules to improve computational performance and for higher clarity of the KB. Control of the reasoning process is assured by several mechanisms, one of the main being metarules specifically designed for almost all the knowledge levels of the KB. We have used public domain knowledge (books, criteria tables) and personal heuristics from one of the authors (Belmonte-Serrano) to implement the KB of RENOIR. In its present form, our KB comprises 1 058 rules, 978 facts, 220 metarules, and 34 modules. A first validation process has shown good performance of the ES compared to 12 physicians with diverse levels of experience in rheumatic diseases. New ongoing versions of the system with improved interfaces and reasoning capabilities are expected before verifying RENOIR's clinical acceptability. © 1994 John Wiley & Sons, Inc.     

Development of two-level decision tree-based real-time scheduling system under product mix variety environment

Most of the research on machine learning-based real-time scheduling (RTS) systems has been aimed toward product constant mix environments. However, in a product mix variety manufacturing environment, the scheduling knowledge base (KB) is dynamic; therefore, it would be interesting to develop a procedure that would automatically modify the scheduling knowledge when important changes occur in the manufacturing system. All of the machine learning-based RTS systems (including a KB refinement mechanism) proposed in earlier studies periodically require the addition of new training samples and regeneration of new KBs. Hence, previous approaches investigating machine learning-based RTS systems have been confronted with the training data overflow problem and an increase in the scheduling KB building time, which are unsuitable for RTS control. The objective of this paper is to develop a KB class selection mechanism that can be supported in various product mix ratio environments. Hence, the RTS KB is developed by a two-level decision tree (DT) learning approach. First, a suitable scheduling KB class is selected. Then, for each KB class, the best (proper) dispatching rule is selected for the next scheduling period. Here, the proposed two-level DT RTS system comprises five key components: (1) training samples generation mechanism, (2) GA/DT-based feature selection mechanism, (3) building a KB class label by a two-level self-organizing map, (4) DT-based KB class selection module, and (5) DT-based dynamic dispatching rule selection module. The proposed two-level DT-based KB RTS system yields better system performance than that by a one-level DT-based RTS system and heuristic individual dispatching rules in a flexible manufacturing system under various performance criteria over a long period.     

A light non-monotonic knowledge-base for service robots

In this paper a Non-Monotonic Knowledge-Base (KB) for practical applications in service robots is presented. The KB is defined as a conceptual hierarchy with inheritance that supports the expression of defaults and exceptions. All classes and individuals, with their properties and relations, can be updated dynamically and the KB-System supports non-monotonic behavior. Non-monotonicity is handled on the basis of a specificity criteria, such that more specific properties and relations have precedence over more general ones. The system supports the expression of conceptual (or terminological) and factual (or assertional) knowledge, which are used in inference in a coherent and consistent way. The KB-System is embedded within the IOCA Architecture, where knowledge about how to communicate and interact with the world, and also knowledge of the particular interpretation situation are represented. The cognitive architecture is structured around a main communication cycle, and queries and conceptual inferences are performed on demand during the interaction of the robot with other agents or the world. The overall structure of the KB with its main interpreter and supporting utilities as well as the embedding of the KB-system in the robot’s architecture are also presented. The KB-System is illustrated with a case study in service robots scenarios, where a practical non-monotonic KB is required. Finally, the implementation of the KB-System in the robot Golem-III is described.     

A Collaborative Environment for Authoring Large Knowledge Bases

Collaborative knowledge base (KB) authoring environments are critical for the construction of high-performance KBs. Such environments must support rapid construction of KBs by a collaborative effort of teams of knowledge engineers through reuse of existing knowledge and software components. They should support the manipulation of knowledge by diverse problem-solving engines even if that knowledge is encoded in different languages and by different researchers. They should support large KBs and provide a scalable and interoperable development infrastructure. In this paper, we present an environment that satisfies many of these goals.We present an architecture for scalable frame representation systems (FRSs). The Generic Frame Protocol (GFP) provides infrastructure for reuse of software components. It is a procedural interface to frame representation systems that provides a common means of accessing and modifying frame KBs. The Generic KB Editor (GKB-EDITOR) provides graphical KB browsing, editing, and comprehension services for large KBs. Scalability of loading and saving time is provided by a storage system (PERK) which submerges a database management system in an FRS. Multi-user access is controlled through a collaboration subsystem that uses a novel optimistic concurrency control algorithm. All the results have been implemented and tested in the development of several real KBs.     

Knowledge-based adaptive agents for manufacturing domains

Modern production systems are increasingly using artificial agents (e.g., robots) of different kinds. Ideally, these agents should be able to recognize the state of the world, to act optimizing their work toward the achievement of a set of goals, to change the plan of action when problems arise, and to collaborate with other artificial and human agents. The development of such an ideal agent presents several challenges. We concentrate on two of them: the construction of a single and coherent knowledge base which includes different types of knowledge with which to understand and reason on the state of the world in a human-like way; and the isolation of types of contexts that the agent can exploit to make sense of the actual situation from a perspective and to interact accordingly with humans. We show how to build such a knowledge base (KB) and how it can be updated as time passes. The KB we propose is based on a foundational ontology, is cognitively inspired, and includes a notion of context to discriminate information. The KB has been partially implemented to test the use and suitability of the knowledge representation for the agent’s control model via a temporal planning and execution system. Some experimental results showing the feasibility of our approach are reported.

     

一种基于Rough集的知识库冗余性化简研究

专家系统知识库的冗余性是影响系统运行效率和知识库维护的一个重要方面。针对一个具体的专家系统——平面几何智能解题系统，分析了关于知识库规则生成时效率低的问题，采用基于粗糙集的化简方法，简化了系统的知识库，减少了知识库的冗余性，提高了系统的效率。