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。     

支持ERP实施的CBR系统的研究

提出了支持ERP实施的CBR系统的框架,并重点研究了其中的关键技术。在案例表示片面,根据ERP实施过程的阶段划分,自顶向下对案例内容进行分解,采用状态-过程-状态的链状结构来表示事件发展的历程和情境信息。在此基础上,采用知识引导策略和最近邻法相结合的混合检索策略进行案例检索。最后给出了一个应用实例进行分析。     

Hybrid similarity measure for case retrieval in CBR and its application to emergency response towards gas explosion

Case retrieval is a primary step in case-based reasoning (CBR). It is important to measure the similarity between each historical case and the target case during the case retrieval process. In recent years, some methods for similarity measure with multiple formats of attribute values can be found in the practical CBR applications, but the in-depth study is still lacking. The objective of this paper is to develop a new method for hybrid similarity measure with five formats of attribute values: crisp symbols, crisp numbers, interval numbers, fuzzy linguistic variables and random variables. First, for each format of the attribute values, the calculation formula to measure the attribute similarity is presented. Then, the method for measuring hybrid similarity between each historical case and the target case is given by aggregating attribute similarities using the simple additive weighting method, and the proper historical case(s) can be retrieved according to the obtained hybrid similarities afterwards. Finally, a case study in the field of emergency response towards gas explosion is introduced to illustrate the use of the proposed method.     

基于CBR的信息系统案例检索多Agent模型研究

将Multi-Agent技术应用于信息系统案例检索中,结合CBR技术与Web Service思想,提出了基于CBR的信息系统案例检索多Agent系统的模型框架和运作流程,设计了基于智能聚类的案例检索算法,通过神经网络的自组织学习优化案例检索的过程,使得该多Agent系统成为具有高度自治性的自我学习与完善的系统,为信息系统案例检索系统的研究与开发提供了一定的借鉴.     

An association-based case reduction technique for case-based reasoning

Case-based reasoning (CBR) is a type of problem solving technique which uses previous cases to solve new, unseen and different problems. Although a larger number of cases in the memory can improve the coverage of the problem space, the retrieval efficiency will be downgraded if the size of the case-base grows to an unacceptable level. In CBR systems, the tradeoff between the number of cases stored in the case-base and the retrieval efficiency is a critical issue. This paper addresses the problem of case-base maintenance by developing a new technique, the association-based case reduction technique (ACRT), to reduce the size of the case-base in order to enhance the efficiency while maintaining or even improving the accuracy of the CBR. The experiments on 12 UCI datasets and an actual case from Taiwan’s hospital have shown superior generalization accuracy for CBR with ACRT (CBR-ACRT) as well as a greater solving efficiency.     

TCBR中案例相关度及案例冗余处理模型

首先介绍基于文档的案例推理技术TCBR,以及CRN、IEs等基本理论,然后详细介绍CRN的构建过程和相关的函数模型,并提出一个求和函数来计算案例与当前问题的相关值,在文章的后面阐述了TCBR的案例学习方法以及案例相似程度的一个判断函数,并提出在这种情况下对案例冗余的处理方案。     

CBR for the management and reuse of image-processing expertise: a conversational system

The development of an image-processing (IP) application is a complex activity, which can be greatly alleviated by user-friendly graphical programming environments. The major objective of the work described in this paper is to help IP experts reuse parts of their applications. A first step towards knowledge reuse has been to propose a suitable representation of the strategies of IP experts by means of IP plans (trees of tasks, methods and tools). This paper describes the CBR module of an interactive system for the development of IP plans. After a brief presentation of the overall architecture of the system and its other modules, the authors explain the distinction between an IP case and an IP plan, and give the selection criteria and functions that are used for similarity calculation. The core of the CBR module is a search/adaptation algorithm, whose main steps are detailed: retrieval of suitable cases, recursive adaptation of the selected one and memorization of new cases. The system’s implementation is presently completed; its functioning is described in a session showing the kind of assistance provided by the CBR module during the development of a new IP application.     

TCBR中案例相关度及案例冗余处理模型

首先介绍基于文档的案例推理技术TCBR，以及CRN、IEs等基本理论，然后详细介绍CRN的构建过程和相关的函数模型，并提出一个求和函数来计算案例与当前问题的相关值，在文章的后面阐述了TCBR的案例学习方法以及案例相似程度的一个判断函数．并提出在这种情况下对案例冗余的处理方案。     

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.     

A review of methods for automatic understanding of natural language mathematical problems

This article addresses the problem of understanding mathematics described in natural language. Research in this area dates back to early 1960s. Several systems have so far been proposed to involve machines to solve mathematical problems of various domains like algebra, geometry, physics, mechanics, etc. This correspondence provides a state of the art technical review of these systems and approaches proposed by different research groups. A unified architecture that has been used in most of these approaches is identified and differences among the systems are highlighted. Significant achievements of each method are pointed out. Major strengths and weaknesses of the approaches are also discussed. Finally, present efforts and future trends in this research area are presented.     

Enhancing information systems management with natural language processing techniques

Natural language and databases are core components of information systems. They are related to each other because they share the same purpose: the conceptualization aspects of the real world in order to deal with them in some way. Natural language processing (NLP) techniques may substantially enhance most phases of the information system lifecycle, starting with requirements analysis, specification and validation, and going up to conflict resolution, result processing and presentation. Furthermore, natural language based query languages and user interfaces facilitate the access to information for anyone and allow for new paradigms in the usage of computerized services. This paper investigates the use of NLP techniques in the design phase of information systems. Then, it reports on data base querying and information retrieval enhanced with NLP.     

Formulating strategies for stakeholder management: a case-based reasoning approach

The management of competing stakeholders has emerged as an important weapon for strategic management. Typically, reactive, defensive, accommodative, or proactive (RDAP) strategies have been employed for getting into the world of stakeholders. This paper proposes a methodology for formulating strategies for stakeholder management by the use of these RDAP strategies. Our methodology consists of four phases: stakeholder analysis, strategy retrieval, strategy revision, and strategy implementation. Strategies are derived on the basis of similar cases stored in case bases. A system called the stakeholder management strategy support system (SMSS) is implemented to put our methodology to work. In order to demonstrate the practical usefulness of this system, Korean Healthcare IT (Information Technology) industry is illustrated. This illustration implies that our methodology is useful, especially in view of dynamic nature of business and its stakeholders. Our methodology will be able to help any business leader create value while navigating a multi-stakeholder environment.     

A case-based reasoning system development for statistical process control: Case representation and retrieval

Statistical process control (SPC) is a sub-area of statistical quality control. Considering the successful results of the SPC applications in various manufacturing and service industries, this field has attracted a large number of experts. Despite the development of knowledge in this field, it is hard to find a comprehensive perspective or model covering such a broad area and most studies related to SPC have focused only on a limited part of this knowledge area. According to many implemented cases in statistical process control, case-based reasoning (CBR) systems have been used in this study for developing of a knowledge-based system (KBS) for SPC to organize this knowledge area. Case representation and retrieval play an important role to implement a CBR system. Thus, a format for representing cases of SPC and the similarity measures for case retrieval are proposed in this paper.     

Evolving CBR and data segmentation by SOM for flow time prediction in semiconductor manufacturing factory

Flow time of semiconductor manufacturing factory is highly related to the shop floor status; however, the processes are highly complicated and involve more than 100 production steps. Therefore, a simulation model with the production process of a real wafer fab located in Hsin-Chu Science-based Park of Taiwan is built for further studying of the relationship between the flow time and the various input variables. In this research, a hybrid approach by combining Self-Organizing Map (SOM) and Case-Based Reasoning (CBR) for flow time prediction in semiconductor manufacturing factory is developed. And Genetic Algorithm (GA) is applied to fine-tune the weights of features in the CBR model. The flow time and related shop floor status are collected and fed into the SOM for clustering. Then, a corresponding SGA-CBR method is selected and applied for flow time prediction. Finally, using the simulated data, the effectiveness of the proposed method (SGA-CBR) is shown by comparing with other approaches.     

基于案例推理的中医诊疗专家系统

把基于案例推理技术用于中医诊疗专家系统的知识表示和推理。提出系统模型,介绍了案例推理的基本结构：案例提取网（easeretrievalnets）以及案例提取算法,对系统中案例的学习和修正机制进行了说明,并提出用基于案例的解释来生成诊断结果的解释性说明以及辅助构建基于案例的中医诊疗辅助教学系统。最后对系统的优点进行总结,并提出进一步研究发展方向。     

Capturing and reusing knowledge in engineering change management: A case of automobile development

The development of complex products, such as automobiles, involves engineering changes that frequently require redesigning or altering the products. Although it has been found that efficient management of knowledge and collaboration in engineering changes is crucial for the success of new product development, extant systems for engineering changes focus mainly on storing documents related to the engineering changes or simply automating the approval processes, while the knowledge that is generated from collaboration and decision-making processes may not be captured and managed easily. This consequently limits the use of the systems by the participants in engineering change processes. This paper describes a model for knowledge management and collaboration in engineering change processes, and based on the model, builds a prototype system that demonstrates the model’s strengths. We studied a major Korean automobile company to analyze the automobile industry’s unique requirements regarding engineering changes. We also developed domain ontologies from the case to facilitate knowledge sharing in the design process. For achieving efficient retrieval and reuse of past engineering changes, we used a case-based reasoning (CBR) with a concept-based similarity measure.

Applications of linguistic techniques for use case analysis

Use cases are effective techniques to express the functional requirements of a system in a very simple and easy-to-learn way. Use cases are mainly composed of natural language (NL) sentences, and the use of NL to describe the behaviour of a system is always a critical point, due to the inherent ambiguities originating from the different possible interpretations of NL sentences. We discuss in this paper the application of analysis techniques based on a linguistic approach to detect, within requirements documents, defects related to such an inherent ambiguity. Starting from the proposed analysis techniques, we will define some metrics that will be used to perform a quality evaluation of requirements documents. Some available automatic tools supporting the linguistic analysis of NL requirements have been used to evaluate an industrial use cases document according to the defined metrics. A discussion on the application of linguistic analysis techniques to support the semantic analysis of use cases is also reported.