A CBR system for injection mould design based on ontology: A case study

Case-based reasoning (CBR) often shows significant promise for improving the effectiveness of design support in mould design, which is a domain strong in practice but poor in theory. However, existing CBR systems lack semantic understanding, which is important for intelligent knowledge retrieval in design support system. This hinders the application of CBR in injection mould design. In order to develop an intelligent CBR system and meet the need of design support for injection mould design, this paper integrates ontology technology into a CBR system by constructing domain ontology as case-base with a new method, in which two means of acquisition are combined, one is acquiring ontology from existing ontologies, the other from established engineering knowledge resources, and proposing a new semantic retrieval method as the first grade case retrieval. Numerical measurement is also employed as the second grade case retrieval, which adopts various methods to calculate different types of attribute values. A case is executed to illustrate the use of proposed CBR system, then a lot of experiments are organized to evaluate its performance and the result shows that the proposed approach outperforms existing CBR systems.     

基于本体的案例推理模型研究*

提出了基于本体的案例检索及相似性评估方法和基于本体的案例适配模型,使得CBR(case-based reasoning)系统的开发可在语义层次上进行相似性评估和案例适配,这样得到的结果更能反映用户的真实需求;并且CBR所需要的领域知识可从本体中获取,大大降低了传统CBR系统中知识获取的瓶颈。最后在此基础上,提出了基于本体的CBR系统模型框架,从软件复用的角度提高了CBR系统的开发效率。     

Case-based reasoning and adaptation in hydraulic production machine design

Case-based reasoning (CBR) can support hydraulic circuit design. Existing expert systems for hydraulic system design use production rules as its source of knowledge. However, this leads to problems of knowledge acquisition and knowledge base maintenance. This paper describes the application of CBR to hydraulic circuit design for production machines, which helps solving problems using past experience. A technique Case-based adaptation (CBA) is implemented in the adaptation stage of CBR so that adaptation becomes much easier. A prototype system has been developed to verify the usefulness of CBR and CBA in hydraulic production machines.     

Towards Automated Case Knowledge Discovery in the M 2 Case-Based Reasoning System

In this paper we present the M ² Case-Based Reasoning (CBR) system. The M ² system addresses a number of issues that present methodologies for CBR systems have shied away from. We discuss techniques for removing the knowledge acquisition bottleneck when acquiring case knowledge. Here, case knowledge refers to the complementary knowledge structures, cases (more specific in nature) and adaptation rules (more general). We address the use of negative cases for updating the case knowledge as well as for refining the similarity measures. In particular we discuss in detail, showing experimental results, the use of Data Mining within the M ² system to build the case base from a database containing operational data, and discover adaptation rules. A methodology to monitor the competence of the CBR system and to utilize negative cases for updating the CBR system to enhance its competence is also discussed. The M ² CBR system also employs Rough Set and Fuzzy Set theories to further enhance its capabilities within real-world applications as well as providing a richer and truer model of human reasoning.     

Hybrid weighted mean for CBR adaptation in mechanical design by exploring effective,correlative and adaptative values

The implementation of case based reasoning (CBR) adaptation in parametric mechanical design can generate the design solution to unknown design problem by adapting similar solutions from other problems already solved. Classical weighted mean (WM) method is a common statistic adaptation method because of its domain independent and easily to be implemented, but with lower adaptation accuracy. A new hybrid WM (HWM) method for CBR adaptation in mechanical design is proposed in this paper, and its contribution is taking advantage of various implicit knowledge hidden in similar case data to improve the performance of WM. To achieve this goal, multiple similarity analysis (MSA), grey relation analysis (GRA) and inductive adaptability analysis (IAA) are firstly used to systematically explore the effective value (EV) of similar case for new design problem, the correlative value (CV) between problem and solution features, and the adaptative value (AV) of similar case's solution element for new adaptation situation, respectively. Then CV, EV and AV compose the integrated weight value of each solution element of similar case in HWM, and the optimal proportion of EV, CV and AV on the integrated weight is also discussed. Based on the parametric transformer design cases, the comparisons of adaptation performances between HWM and other statistical and intelligent methods were carried out, and the empirical results show that HWM has the better adaptation performance than other comparative methods by comparing the adaptation accuracy.     

范例推理中的知识发现技术

范例推理中有许多相关的知识 ,相应地有知识获取过程 ,其中也存在一定程度的知识获取瓶颈问题 .本文着重探讨在范例推理系统中引入一系列可以使用的知识发现技术 ,以期提高范例推理系统的知识获取的自动化程度 ;本文针对提出的两类算法 ,进行了实验与讨论     

A unified logical model for CBR‐based e‐commerce systems

This article examines new issues resulting from applying case‐based reasoning (CBR) in e‐commerce and proposes a unified logical model for CBR‐based e‐commerce systems (CECS) that consists of three cycles and covers almost all activities of applying CBR in e‐commerce. This article also decomposes case adaptation into problem adaptation and solution adaptation, which not only improves the understanding of case adaptation in the traditional CBR, but also facilitates the refinement of activity of CBR in e‐commerce and intelligent support for e‐commerce. It then investigates CBR‐based product negotiation. This article thus gives insight into how to use CBR in e‐commerce and how to improve the understanding of CBR with its applications in e‐commerce from a logical viewpoint. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 29–46, 2005.     

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.     

基于ALCQ(D)的CBR事例修正算法研究

CBR(基于事例推理)是人工智能领域的一个分支,它克服了知识获取的瓶颈问题,事例修正是CBR的关键步骤。以ALC为代表的描述逻辑已被充分应用到CBR中,但目前在基于描述逻辑的CBR中还没有比较有效的算法来判断检索到的相似事例是否需要修正和如何进行修正。ALCQ(D)是在ALC的基础上引入定性数量约束Q和有型域D得到的。提出的算法用ALCQ(D)概念来描述CBR源事例和目标事例,先假定检索到的相似事例能够解决目标问题,即假定目标事例和相似事例同时满足知识库,但这样可能会与知识库产生冲突;接着使用冲突检测机制来查找相似事例概念描述中导致冲突的概念;最后使用概念替换规则在TBox本体库中检索该概念的最相似概念去替换它自己。研究表明,该算法具有界限性、可靠性和完备性。通过一个实例对其进行检验,结果表明,该算法可以准确修正检索到的相似事例,解决目标问题。     

Similarity and metrics in case‐based reasoning

Similarity is a core concept in case‐based reasoning (CBR), because case base building, case retrieval, and even case adaptation all use similarity or similarity‐based reasoning. However, there is some confusion using similarity, similarity measures, and similarity metrics in CBR, in particular in domain‐dependent CBR systems. This article attempts to resolve this confusion by providing a unified framework for similarity, similarity relations, similarity measures, and similarity metrics, and their relationship. This article also extends some of the well‐known results in the theory of relations to similarity metrics. It appears that such extension may be of significance in case base building and case retrieval in CBR, as well as in various applied areas in which similarity plays an important role in system behavior. © 2002 Wiley Periodicals, Inc.     

An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry

With the increasing ageing population worldwide, providing effective nursing care planning in nursing homes is important in meeting the expectations of elderly patients and in streamlining the healthcare information process, hence maintaining high‐quality services. Instead of the traditional manual nursing care planning formulation based on expert experience and subjective judgement, this paper describes an adaptive decision support system, namely, the cloud‐based nursing care planning system, to enable decision making in formulating nursing care strategies. By integrating cloud computing technology and the case‐based reasoning (CBR) technique, medical records and documents pertaining to the elderly can be captured in real time, whereas appropriate treatment plans based on past similar treatment records can be formulated. However, the current case adaptation processes in CBR rely on domain experts to modify retrieved cases, which may not satisfy the needs of the elderly. Therefore, text mining is integrated in the case adaptation process of CBR for extracting up‐to‐date medical information from the Internet so that its efficiency can be improved. By conducting a pilot study in a nursing home, it was shown that the time for formulating applicable treatment plans for elderly patients can be reduced, and the service satisfaction level can be enhanced.     

Incorporating domain-specific knowledge into a genetic algorithm to implement case-based reasoning adaptation

In case-based reasoning systems the adaptation phase is a notoriously difficult and complex step. The design and implementation of an effective case adaptation algorithm is generally determined by the type of application which decides the nature and the structure of the knowledge to be implemented within the adaptation module, and the level of user involvement during this phase. A new adaptation approach is presented in this paper which uses a modified genetic algorithm incorporating specific domain knowledge and information provided by the retrieved cases. The approach has been developed for a CBR system (CBEM) supporting the use and design of numerical models for estuaries. The adaptation module finds the values of hundreds of parameters for a selected numerical model retrieved from the case-base that is to be used in a new problem context. Without the need of implementing very specific adaptation rules, the proposed approach resolves the problem of acquiring adaptation knowledge by combining the search power of a genetic algorithm with the guidance provided by domain-specific knowledge. The genetic algorithm consists of a modifying version of the classical genetic operations of initialisation, selection, crossover and mutation designed to incorporate practical but general principles of model calibration without reference to any specific problems. The genetic algorithm focuses the search within the parameters' space on those zones that most likely contain the required solutions thus reducing computational time. In addition, the design of the genetic algorithm-based adaptation routine ensures that the parameter values found are suitable for the model approximation and hypotheses, and complies with the problem domain features providing correct and realistic model outputs. This adaptation method is suitable for case-based reasoning systems dealing with numerical modelling applications that require the substitution of a large number of parameter values.     

基于适应性修改的CBR建库中的数据挖掘

研究了从大量的原始数据中挖掘典型和关键事例以进行CBR事例库建造的问题,指出在不同的数据库结构中,其算法不同,没有一个通用的算法能最好地满足所有的领域。并引用适应性修改的概念提供了在CBR中的关键事例的算法。     

Competence Models and the Maintenance Problem

Case-based reasoning (CBR) systems solve problems by retrieving and adapting the solutions to similar problems that have been stored previously as a case base of individual problem solving episodes or cases. The maintenance problem refers to the problem of how to optimize the performance of a CBR system during its operational lifetime. It can have a significant impact on all the knowledge sources associated with a system (the case base, the similarity knowledge, the adaptation knowledge, etc.), and over time, any one, or more, of these knowledge sources may need to be adapted to better fit the current problem-solving environment. For example, many maintenance solutions focus on the maintenance of case knowledge by adding, deleting, or editing cases. This has lead to a renewed interest in the issue of case competence, since many maintenance solutions must ensure that system competence is not adversely affected by the maintenance process. In fact, we argue that ultimately any generic maintenance solution must explicitly incorporate competence factors into its maintenance policies. For this reason, in our work we have focused on developing explanatory and predictive models of case competence that can provide a sound foundation for future maintenance solutions. In this article we provide a comprehensive survey of this research, and we show how these models have been used to develop a number of innovative and successful maintenance solutions to a variety of different maintenance problems.     

推理建模中基于KDD和粗糙集的案例修改

在应用基于案例推理技术进行智能建模时,案例修改后的案例质量好坏直接影响所建模型的精度,但是由于案例修改对领域知识的依赖性很强,采用一般手工案例修改方法尤法保证案例修改的质量,即无法保证智能推理模型的精度。基于以上原因,该文提出了一种新的案例修改方法,利用KDD技术,通过有效的多值关联规则挖掘算法从运行数据库中挖掘出案例各属性间的依赖关系,得到案例修改的基本关联规则集,在此基础上利用粗糙集理论对基本关联规则集进行简约,然后根据简约后的关联规则进行案例修改。在线对比实验证明,应用本文方法进行案例修改,提商了修改后的案例质量,从而提高了整体智能推理模型的精度。     

Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.     

Design and implementation of a case-based reasoning system for marketing plans

Unstructured intangible experiences and knowledge are usually difficult to represent and instantiate, which engenders the hardship of knowledge transfer and sharing. Past marketing plans are such valuable documents containing strategic planning knowledge and experiences.Case-Based Reasoning (CBR), which consists of retrieving, reusing, revising, and retaining cases, has been proved effective in retrieving information and knowledge from prior situations and being widely researched and applied in a great variety of problem territories.This paper targets at designing a CBR architecture and a method that facilitate the sharing and retrieving of cases of great concern to the marketing personnel. After an intensive survey of CBR methods and applications, a CBR system embedding multi-attribute decision making method, which provides both overall similarity level and similarity level of each selected attribute, is proposed to enhance the adaptation of a new marketing plan. In addition, a multi-attribute gap analysis diagram is developed to visualize the similarity along with the gap between candidate and target cases, so as to better support interaction and group decision making in the process of strategically formulating a new marketing plan. The CBR system was implemented and successfully demonstrated on case retrieval of a telecommunication company.