实例推理和规则推理在实例修改中的应用

在CBR系统中实例修改是一个关键环节,该文通过分析几种实例修改的方法,提出了将实例推理和规则推理进行整合后引入到实例修改过程中,建立修改规则库来完成实例修改,并就如何建立修改规则库进行了说明,为建立智能化的实例修改提供一种思路。     

基于实例的推理在装配过程设计中的应用

通过研究确定装配顺序的方法,以装配零部件构成和确定的装配顺序为输入,采用装配功能树到装配过程机制树映射的方法实现装配过程设计。在此基础上提出从功能需求、行为模型和过程机制模型3个层次对装配实例进行描述的方法和装配过程实例相似性系数的计算方法。实例运行结果验证了该方法的有效性。     

一个面向产品变型设计过程的知识处理方法模型

变型设计是在产品工作原理、基本功能定义不变的情况下,通过对现有产品局部结构型式和尺寸的变异来满足不同工作性能需求的一类设计行为.与创造性设计和适应性设计比较,产品变型设计是一种相对刻板、固定同时应用又十分广泛的设计行为.本文在分析了产品变型设计的基本求解流程之后,提出了一个以基于实例的推理为核心,综合运用规则推理和约束满足方法的知识处理方法模型,并且,结合所开发的"细纱机模块化变型设计系统",从知识表达模式和推理过程两方面对其特点和应用作出了介绍.     

基于知识库和实例推理的构件检索方法

提出了一种利用人工智能领域中基于实例的推理（CBR）创建基于知识库的软件构件库进行构件检索的框架方法。重点阐述了利用软件构件的功能和行为知识表达测量检索到的构件实例与问题需求的相似度、构件功能性和构件可重用性的方法。     

基于实例推理的模具设计技术研究

模具设计需要大量借鉴以往的设计方案。对设计经验的合理组织和重用可以缩短模具设计周期,提高模具设计效率。文章介绍了一种基于实例推理技术的模具设计方法:通过状态空间法表示了设计实例;阐述了基于相似度理论的最近邻居算法检索策略,能够对实例库中的实例进行检索。并以凸模零件为例对所讲述的方法及技术进行了说明,结果表明实例推理技术的应用可以提高模具的设计效率。     

一种基于实例的软件概要设计方法

本文提出了一个基于实例的软件概要设计的方法和实现过程。该方法是基于系统所记忆的以往软件概要设计的实例,来推理解决所面临的软件概要设计问题。它提高了软件概要设计的有效性和可操作性。     

基于本体的分布式实例推理技术研究

为了克服单一实例库知识的局限性,实现分布式环境下多数据源的知识重用和共享,提出了一个分布式实例推理系统框架.系统通过本体服务器建立和维护实例库之间的本体知识,其中基本本体为知识的表示提供了全局约束和基础,实例推理服务器可以在基本本体框架下定义领域本体来灵活表达各自的领域知识,而本体目录则为知识的检索提供了向导.本体的引入解决了不同实例库之间知识的互理解和互操作性,能够有效地实现多实例库的协同推理.系统采用Web Service技术构建,是一个开放的系统框架,具有很强的可扩展性.     

基于模糊粗糙集的设计实例库维护方法及应用

实例库维护是基于实例的推理方法中的一项核心工作,目前国内外关于实例库维护方法的研究还存在一些不足.针对机械产品设计实例的特点,提出一种基于模糊粗糙集理论的实例库维护方法.首先定义了产品设计实例特征权重计算、实例库族集的划分以及实例属性的约简方法;在此基础上,引入覆盖度和可触及方法对典型实例进行筛简.最后开发了面向工程机械设计的实例库维护系统,并给出了具体应用效果.     

基于规则与基于实例的集成推理研究

基于实例推理（ＣＢＲ）与基于规则推理（ＲＢＲ）在许多决策支持系统中已得到了广泛的应用。如何有效地提高那些既含有演绎信息又含有类比信息的问题求解效率亦是专家系统开发者的研究课题之一。本文分析了ＣＢＲ和ＲＢＲ的优缺点，回顾了这两种推理方式的工作原理。针对ＲＢＲ与ＣＢＲ应用的局限性，本文提出了一种新的推理模式──ＲＢＲ与ＣＢＲ集成推理。这种模式既利用了ＣＢＲ的长处又利用了ＲＢＲ的优点，力图提高对含有不完整领域知识的问题的推理效率。     

基于实例推理的产品概念设计模型研究

产品概念设计是设计过程中最具创造性的阶段。文中分析了产品概念设计过程的特点及其主要研究内容 ,介绍了基于实例推理的产品概念设计模型结构 ,对其关键技术进行了研究 ,提出了产品概念设计的实例表示、功能推理和方案评价模型。     

基于案例推理的ISP知识重用方法

针对企业信息化中信息系统规划(ISP)在较大程度上依赖先前规划经验和知识的特点,为有效支持ISP知识重用和管理创新,将案例推理技术应用于ISP知识重用的全过程,提出基于案例推理的ISP知识重用方法(HRM KGANNA),研究应用框架、案例表示、案例检索算法与重用等关键技术。通过组织案例库进行ISP知识检索实验,结果表明HRM KGANNA法具有良好的准确性和较高的效率。     

Maintaining Retrieval Knowledge in a Case-Based Reasoning System

The knowledge stored in a case base is central to the problem solving of a case-based reasoning (CBR) system. Therefore, case-base maintenance is a key component of maintaining a CBR system. However, other knowledge sources, such as indexing and similarity knowledge for improved case retrieval, also play an important role in CBR problem solving. For many CBR applications, the refinement of this retrieval knowledge is a necessary component of CBR maintenance. This article focuses on optimization of the parameters and feature selections/weights for the indexing and nearest-neighbor algorithms used by CBR retrieval. Optimization is applied after case-base maintenance and refines the CBR retrieval to reflect changes that have occurred to cases in the case base. The optimization process is generic and automatic, using knowledge contained in the cases. In this article we demonstrate its effectiveness on a real tablet formulation application in two maintenance scenarios. One scenario, a growing case base, is provided by two snapshots of a formulation database. A change in the company's formulation policy results in a second, more fundamental requirement for CBR maintenance. We show that after case-base maintenance, the CBR system did indeed benefit from also refining the retrieval knowledge. We believe that existing CBR shells would benefit from including an option to automatically optimize the retrieval process.     

A Case-Based Framework for Interactive Capture and Reuse of Design Knowledge

Aerospace design is a complex task requiring access to large amounts of specialized information. Consequently, intelligent systems that support and amplify the abilities of human designers by capturing and presenting relevant information can profoundly affect the speed and reliability of design generation. This article describes research on supporting aerospace design by integrating a case-based design support framework with interactive tools for capturing expert design knowledge through concept mapping. In the integrated system, interactive concept mapping tools provide crucial functions for generating and examining design cases and navigating their hierarchical structure, while CBR techniques facilitate retrieval and aid interactive adaptation of designs. Our goal is both to provide a useful design aid and to develop general interactive techniques to facilitate case acquisition and adaptation. Experiments illuminate the performance of the system's context-sensitive retrieval during interactive case adaptation and the conditions under which it provides the most benefit.     

Automating the Diagnosis and Rectification of Deflection Yoke Production Using Hybrid Knowledge Acquisition and Case-Based Reasoning

Deflection yoke (DY) is one of the main components of the color display tube (CDT) that determines the image quality of a computer monitor. Once a DY anomaly is found during production, the remedy process is performed in two steps: identifying the type of anomaly from the observed problem pattern and adjusting manufacturing process parameters to rectify it. To support this process, we introduce a knowledge-based system using a hybrid knowledge acquisition technique and case-based reasoning. The initial phase of the knowledge acquisition employs a systematic and quantitative data processing including stepwise regression and an inductive learning algorithm. This automated expertise elicitation produces strategies, which are represented by decision trees or if-then rules, to specify DY anomalies from display patterns. The strategies are then refined by introducing human expertise. The knowledge acquisition process was designed to support for this cognitive cooperation. For coordinating the process parameters to remedy the specified anomalies, a case-based reasoning is utilized. The laboratory and field test proved that the developed knowledge-based system could produce highly effective decisions for the process control in DY production.     

An Exception Handling of Rule-Based Reasoning Using Case-Based Reasoning

In this paper, we propose the CCAR (Combining Case-based And Rule-based reasoning) model for an exception handling of Rule-based Reasoning using Case-based Reasoning. The central idea of the model proposed in this paper is to represent the exception of a rule as a case, and to utilize the case for a solution to a problem, and then to search the case memory to retrieve a case which violates the conclusion of a rule. If the similarity between a target problem and the selected case is high, the conclusion of a case is applied. Otherwise, the conclusion of rule-based reasoning is applied.     

Characterisation of a Novel Indexing Technique for Case-Based Reasoning

In this paper, we investigate two novel indexing schemes called D-HS and D-HS+PSR(II) designed for use in case-based reasoning systems. D-HS is based on a matrix of cases indexed by their discretised attribute values. D-HS+PSR(II) extends D-HS by combining the matrix with an additional tree-like indexing structure to allow for solution reuse. D-HS+PSR(II)s novelty lies in its ability to improve retrieval efficiency over time by reusing previously encountered solution patterns. Benefits include simplicity, accuracy, speed, robustness to missing values and ability to facilitate efficient real time maintenance of retrieval knowledge as the size of the case-base grows. We present empirical results from an analyses of 20 case-bases and demonstrate the techniques to be of similar competency to C4.5 yet much more efficient. Performance advantages over C4.5 are shown to be especially apparent when tested on case-bases which grow in size over time or those with missing values.     

Case-Based Knowledge Management Tools for Software Development

Modern software development is a knowledge-intensive activity. The proliferation of development tools, rapidly changing technology, and increasing complexity and diversity of application domains all increase the cognitive burden placed on software developers. General purpose programming languages and CASE tools offer little relief from these problems. Knowledge management tools are needed that can effectively capture and disseminate software development knowledge that applies to the domain-specific needs of an organization. This knowledge is not static, but evolves with technology and the changing needs of the organizations development practices, customer base, and business milieu.This paper presents an infrastructure that supports evolving knowledge through case-based techniques and domain analysis methods that capture emerging knowledge and synthesize it into generally applicable forms. The approach is less concerned with the veracity of knowledge in its repository than evolving the knowledge toward answers to problems that fit the organizations technical and business context. Implications of this approach go beyond supporting software development to other knowledge-intensive professions where knowledge management tools can be used to support an organizational memory.     

Conversational Case-Based Reasoning

Conversational case-based reasoning (CCBR) was the first widespread commercially successful form of case-based reasoning. Historically, commercial CCBR tools conducted constrained human-user dialogues and targeted customer support tasks. Due to their simple implementation of CBR technology, these tools were almost ignored by the research community (until recently), even though their use introduced many interesting applied research issues. We detail our progress on addressing three of these issues: simplifying case authoring, dialogue inferencing, and interactive planning. We describe evaluations of our approaches on these issues in the context of NaCoDAE and HICAP, our CCBR tools. In summary, we highlight important CCBR problems, evaluate approaches for solving them, and suggest alternatives to be considered for future research.