基于Agent和CBR的电子商务谈判系统模型研究*

提出一种融合了多Agent和案例推理（CBR）技术的电子商务谈判系统模型,在多Agent环境下应用CBR技术捕获并重用以前成功的谈判案例,从中提取适应性策略来为交易提供决策支持,这些策略可以根据所处环境的改变动态生成。对相关问题进行了讨论,包括谈判案例的匹配和谈判策略的选择。     

案例推理的故障诊断技术研究综述

基于案例推理(case-based reasoning,CBR)的故障诊断作为一种新的智能诊断技术,模拟人类求解问题的思路,通过历史案例发现新问题的解。概述了CBR的理论基础和基本原理;从工作过程和集成框架两个方面综述了CBR故障诊断技术的研究现状,其中工作过程包括案例的表示、检索和重用,以及案例库的维护,集成框架包括CBR与基于规则推理、CBR与人工神经网络以及CBR与多智能体等三种情况;比较了六种故障诊断技术的特点及应用范围;总结了CBR故障诊断技术有待解决的问题。     

灰度关联理论在CBR中的应用研究

针对基于规则推理技术(RBR)知识获取困难、自学习能力差等缺陷,将基于案例推理技术(CBR)引入故障诊断系统中.介绍了基于案例推理的故障诊断方法的工作机理和过程模型,阐述了案例表示、案例检索、案例保存和案例库维护机制,然后简单介绍了灰色关联理论知识,并把灰色关联理论应用到故障案例相似度的计算中.根据实验结果可知,该方法有效地改进了案例检索算法,提高了故障案例匹配的准确度和检索效率,同时具有较好的分辨率.     

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。     

灰度关联理论在CBR中的应用研究

针对基于规则推理技术（RBR）知识获取困难、自学习能力差等缺陷,将基于案例推理技术（CBR）引入故障诊断系统中。介绍了基于案例推理的故障诊断方法的工作机理和过程模型,阐述了案例表示、案例检索、案例保存和案例库维护机制,然后简单介绍了灰色关联理论知识,并把灰色关联理论应用到故障案例相似度的计算中。根据实验结果可知,该方法有效地改进了案例检索算法,提高了故障案例匹配的准确度和检索效率,同时具有较好的分辨率。     

.基于遗传算法的全局优化检索策略研究*

案例检索是基于案例推理(CBR)系统中的关键技术,也是实现智能挖掘系统的关键环节。为了能够进一步提高案例检索效率与准确性,传统研究多是从案例属性和案例库的约减两方面入手,但是没有考虑案例属性权重。提出了一种基于遗传算法的全局优化案例检索模型,该模型利用遗传算法在搜索优化上的优势,对案例库、 属性权重、K-NN中的K值进行全局同步优化。最后,通过实验验证了该模型在检索效率与准确性上优于传统模型。     

CBR技术在森林火灾预报中的应用

本文描述了一个基于CBR技术的森林火灾预报系统首先介绍整个系统构架和功能,而后对系统实现中的关键问题进行分析解决,包括案例的表示,案例检索及基于规则的推理。     

CBR技术在森林火灾预报中的应用

本文描述了一个基于CBR技术的森林火灾预报系统.首先介绍整个系统构架和功能,而后对系统实现中的关键问题进行分析解决,包括案例的表示,案例检索及基于规则的推理.     

一种改进的案例推理分类方法研究

特征属性的权重分配和案例检索策略对案例推理（Case-based reasoning,CBR）分类的准确率有显著影响. 本文提出一种结合遗传算法、内省学习和群决策思想改进的CBR分类方法. 首先,利用遗传算法得到多组属性权重,再根据内省学习原理对每组权重进行迭代调整;然后,通过案例群检索策略得到满足大多数原则的群决策分类结果;最后,以典型分类数据集的对比实验证明了本文方法能进一步提高CBR分类的准确率. 这表明内省学习可以保证权重分配的合理性,案例群检索策略能充分利用案例库的潜在信息,对提升CBR的学习能力有显著作用.     

基于ANN与CBR相结合的复杂装备故障诊断研究

在分析了神经网络(ANN)方法与案例推理(CBR)方法的特点和互补性的基础上,设计了基于ANN与CBR相结合的复杂装备故障诊断模型.将人工神经网络方法融入CBR推理的故障库分类、案例检索、案例修改等多个阶段中,较好地解决了复杂电子装备故障诊断的快速与准确问题.最后通过对雷达情报综合电子信息系统故障实例的诊断仿真,验证了该算法的有效性.     

Hierarchical case-based reasoning integrating case-based anddecompositional problem-solving techniques for plant-control softwaredesign

Case based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, so most CBR systems dealing with complex problem solving tasks have to use multiple cases. The paper describes and evaluates the technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design     

多维优化案例推理检索算法研究

案例检索是案例推理系统的中心环节,检索质量关系着整个系统的质量。利用遗传算法GA和层次分析法AHP相结合,从案例库,属性的约简,权值确定三方面对案例检索进行优化。利用遗传算法在搜索优化上的优势,使用两维的编码结合权值从而形成三维优化,并利用经验和权值中间表进行权值学习。从而提高检索命中率。并将这种模型运用到基于旅游的多策略数据挖掘系统进行实验,结果表明在案例检索的命中率上有明显提高。     

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.     

分布式环境下基于语义相似的案例检索

分布式环境下的异构案例表达制约了案例检索过程中案例属性之间的可比性，进而成为分布式环境下案例推理系统成败的一个关键问题。该文提出基于语义相似的案例检索，通过利用Ontology技术来理解案例属性的内在含义，在此基础上定义并计算属性之间的相似程度。对原型系统的初步测试证明了基于语义相似的案例检索有效性。     

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.     

P2P case storage and retrieval with an unspecified ontology

Traditional approaches for similarity-based retrieval of structured data, such as Case-Based Reasoning (CBR), have been largely implemented using centralized storage systems. In such systems, when the cases contain both numeric and free-text attributes, similarity-based retrieval cannot exploit standard speedup techniques based on multi-dimensional indexing, and the retrieval is implemented by an exhaustive comparison of the case to be solved with the whole set of stored cases. In this work, we review current research on Peer-to-Peer (P2P) and distributed CBR techniques and propose a novel approach for storage of the case-base in a decentralized Peer-to-Peer environment using the notion of Unspecified Ontology to improve the performance of the case retrieval stage and build CBR systems that can scale up to large case-bases. We develop an algorithm for efficient retrieval of approximated most-similar cases, which exploits inherent characteristics of the unspecified ontology in order to improve the performance of the case retrieval stage in the CBR problem solving cycle. The experiments show that the algorithm successfully retrieves cases close to the most-similar cases, while reducing the number of cases to be compared. Hence, it improves the performance of the retrieval stage. Moreover, the distributed nature of our approach eliminates the computational bottleneck and single point of failure of the centralized storage systems.     

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.     

Abductive case‐based reasoning

This article introduces abductive case‐based reasoning (CBR) and attempts to show that abductive CBR and deductive CBR can be integrated in clinical process and problem solving. Then it provides a unified formalization for integration of abduction, abductive CBR, deduction, and deductive CBR. This article also investigates abductive case retrieval and deductive case retrieval using similarity relations, fuzzy similarity relations, and similarity metrics. The proposed approach demonstrates that the integration of deductive CBR and abductive CBR is of practical significance in problem solving such as system diagnosis and analysis, and will facilitate research of abductive CBR and deductive CBR. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 957–983, 2005.     

基于案例推理的网络舆情辅助决策系统研究

为了实现网络舆情监测与辅助决策,提出一种基于案例推理的网络舆情辅助决策系统框架。首先综述网络舆情研究现状,提出基于案例推理的网络舆情辅助决策系统的意义;其次,研究基于案例推理的网络舆情辅助决策系统的工作流程,并提出基于案例推理的网络舆情辅助决策系统的框架;接着研究基于案例推理的网络舆情辅助决策系统中的关键内容,即案例表示与检索,并给出详细实现方法;最后,在以上研究基础上实现原型系统,结果表明该系统可以有效实现舆情辅助决策。