基于文本案例的推理技术综述

首先介绍了CBR(Case-based reasoning)推理中一个重要分支--基于文本案例的推理技术TCBR(Textual CBR)的产生背景.然后详细介绍了TCBR推理时采用的结构--实例提取网CRN(Case Retrieval Nets),描述案例的知识单元--信息实体的概念和特性,以及TCBR中如何进行实例提取、改编和学习.最后,对TCBR的改进研究、代表项目以及未来的发展方向作了概括性说明.     

一个基于实例推理的专家系统

该文研究了基于实例推理（Ｃａｓｅ－ＢａｓｅｄＲｅａｓｏｎｉｎｇ,简称ＣＢＲ）的机制,重点讨论了其在商业ＭＩＳ框架生成专家系统ＦＧＳＭ中的应用,结合ＦＧＳＭ系统的研制,给出了ＣＢＲ的一般实现过程。     

CASE-BASED REASONING IN ENVIRONMENTAL MONITORING APPLICATIONS

Environmental monitoring is usually based on large volumes of data, while in general, environmental decision making is a complex problem, has a high degree of uncertainty, and involves diverse areas of expertise. Environmental decision-support systems are therefore good candidates for application of artificial intelligence (AI) techniques. In this paper it is argued that a suitable approach for building these systems is the use of case-based reasoning or analogical reasoning techniques, which offer more adaptability and better explanation facilities than other AI paradigms. As an example, the development stages, the architecture, and the operational characteristics of the expert system Air Quality Predictor (AIRQUAP), developed to predict air pollution levels in Athens, Greece, are described. AIRQUAP helps users retrieve historical data intelligently and can predict air pollution levels, useful for management of air pollution episodes. The performance of the system is also compared with other techniques used in this class of applications.     

XPLANS: CASE-BASED REASONING FOR PLAN RECOGNITION

In the context of a command, control, communication, and intelligence (Cff) system, we have built a case-based reasoning module for plan recognition. It is based on knowledge representation structures called XPlans, inspired in part by Schank's Explanation Patterns. The uncertainty inherent to an uncontrolled flow of input and the presence of lacunar data make the retrieval of cases difficult. This led us to develop an algorithm for partial and progressive matching of the target case onto some of the source cases. This matching amounts in practice to a credit assignment mechanism, included in the algorithm associated with each XPlan. This method has been designed to meet the requirements of a DRET project to build a decision support module for a C3I system— the MATIS project. Its task is to interpret and complete the results of an intelligent “ pattern-recognition-and-data-fusion” module in order to make the intentions underlying the recognized situation explicit to the decision maker. This advice is given as a causal explanation of an agent's behavior from low-level information.     

CASE-BASED REASONING FOR MANAGING NONCOMPLIANCE WITH CLINICAL GUIDELINES

Despite the recognized advantages that can be obtained in clinical practice when following clinical guidelines (GL), situations of noncompliance with them may emerge. Keeping track of such deviations from the default GL execution, and documenting the physician's motivations, would clearly be an added value. Moreover, repeated alterations of GL actions (or flow) may highlight the need for an adaptation of the GL itself to the local reality, or may even indicate an improper or weak initial GL definition.
In this article, we propose an approach for managing noncompliance with GL, based on the case-based reasoning methodology. In front of a new noncompliance case, our tool allows the physician to retrieve past situations similar to the current one, and to decide whether to reapply the same GL modifications adopted in them. Moreover, the tool is able to learn indications from the ground noncompliance cases that can be deployed for local adaptation, and possibly, for suggesting more formal GL revisions to be carried out by a committee of expert physicians.     

一个基于范例推理的专家系统ICMIX

蛋鸡饲料配方是一个要求考虑多种制约因素，需要丰富的富实践经济的问题域。本文介绍的ＩＣＭＩＸ是基于范例推理的蛋鸡饲料配方专家系统，其范例向面规划模型，由日粮类型子范例和营养标准子范例构成。     

A CASE-BASED REASONING APPROACH TO AUTOMATED WELD-PROCESS DESIGN

In this article, we present a case-based-reasoning approach for the solution of complex manufacturing problems. We describe an application of this methodology to the development of an automated system for weld-process design. We also describe the generic system design and implementation of a Weld-Process Design Advisor (WPDA). The uniqueness of this system is that it combines case-based and heuristic rule-based reasoning methods with analytic models of the problem domain.     

基于范例推理中的一种范例匹配方法模型

传统的最近邻检索和匹配算法不一定能找到最优相似范例,本文提出了一种根据范例间数值型属性对应成比例的原则进行范例匹配的方法,它作为原算法的一个补充,为找到最优相似范例提供了一条有效途径.     

嵌入元级结构的基于事例推理研究

元级推理是智能软件系统中的一种重要机制。本文首先讨论元级推理的作用,原理以及实现策略,然后重点介绍了我们新近的一个面向事例诊断的推理器ＭＧＣＢＤ。本文各节介绍了ＭＧＣＢＤ的知识表示,元级推理框架以及事例检索,匹配和适配过程的设计方法和技术特点,并用一个具体裕列来说明ＭＧＣＢＤ的推理功能。     

CASE-BASED REASONING FOR PREDICTING MULTIPERIOD FINANCIAL PERFORMANCES OF TECHNOLOGY-BASED SMEs

Recently, various types of technology funds became available to support the programs for technology development and commercialization of SMEs (Small and Medium Enterprise) in Korea. However, the potential financial performances have not been sufficiently considered at the selection stage of fund recipient SMEs whereas the default risk has been a major concern. This article proposes a Case Based Reasoning (CBR) system with Genetic Algorithm (GA) for predicting the Exponentially Weighted Moving Average (EWMA) of multiperiod financial performances of technology-oriented SMEs. It is expected that the proposed model can be applied to a wide range of technology investment-related decision-making procedures.     

基于案例推理的上市公司高管人事风险预警

近年来不断发生的高管违规事件,使监管者和投资者越来越意识到防范上市公司非经营性财务风险的重要性.为了分析和识别这些风险,建立了基于案例推理技术的上市公司高管人事风险预警,研究了其中的关键技术.最后通过实例分析对该技术进行检验,研究表明,将该方法应用于上市公司风险预警是有效的.     

CASE-BASED REASONING AND KNOWLEDGE DISCOVERY IN MEDICAL APPLICATIONS WITH TIME SERIES

This paper discusses the role and integration of knowledge discovery (KD) in case-based reasoning (CBR) systems. The general view is that KD is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining that is mostly related to case-specific experience, KD aims at the elicitation of new knowledge that is more general and valuable for improving the different CBR substeps. KD for CBR is exemplified by a real application scenario in medicine in which time series of patterns are to be analyzed and classified. As single pattern cannot convey sufficient information in the application, sequences of patterns are more adequate. Hence it is advantageous if sequences of patterns and their co-occurrence with categories can be discovered. Evaluation with cases containing series classified into a number of categories and injected with indicator sequences shows that the approach is able to identify these key sequences. In a clinical applica-tion and a case library that is representative of the real world, these key sequences would improve the classification ability and may spawn clinical research to explain the co-occurrence between certain sequences and classes.     

VISIO-SPATIAL CASE-BASED REASONING: A CASE STUDY IN PREDICTION OF PROTEIN STRUCTURE

We show that visio-spatial representations and reasoning can be used as a similarity metric for case-based protein structure prediction. Our system retrieves pairs of α-helices based on contact map similarity, then transfers and adapts the structure information to an unknown helix pair. We show that similar protein contact maps predict a similar three-dimensional protein structure. The success of this method provides support for the notion that changing representations can enable similarity metrics in case-based reasoning.     

INTRODUCTION TO THE SPECIAL ISSUE ON CASE-BASED REASONING IN THE HEALTH SCIENCES

There has been an explosion of interest in health sciences applications of case-based reasoning (CBR), not only in the traditional CBR in medicine domain, but also in bioinformatics, enabling home health-care technologies, CBR integration, and synergies between CBR and knowledge discovery. This special issue features the best papers from the third workshop on CBR in the health sciences, held at ICCBR-05 in Madrid. It is the third in a series of exciting workshops, the first two of which were held at ICCBR-03, in Trondheim, Norway, and at ECCBR-04, in Madrid, Spain. The nine high-quality papers introduced here represent the research and experience of twenty-two authors working in eight different countries on a wide range of problems and projects. These papers illustrate some of the major trends of current research in CBR in the health sciences, and represent overall an excellent sample of the most recent advances of CBR in the health sciences.     

A CASE-BASED REASONING APPROACH TO THE IDENTIFICATION OF MATERIALS FROM DIFFRACTION PATTERNS

X-ray diffractometry, within materials engineering, is a promising area of application for case-based reasoning. A large database of spectral diffraction patterns includes entries with different quality marks; moreover, several diffraction patterns happen to be equivalent, identifying the same material (crystalline phase), even though it also happens, that a spectral diffraction pattern alone would not identify a crystalline phase, and parameters such as density also have to be involved for identification. Current practice in the scanning and processing of so-called powder diffraction files, out of a database of files (formerly cards), calls for improvements of various kinds. Arguably, case-based reasoning is a technique from within AI that appears to exhibit a very interesting potential to make the process of identification less cumbersome.     

PROTOTYPICAL CASES FOR RETRIEVAL, REUSE, AND KNOWLEDGE MAINTENANCE IN BIOMEDICAL CASE-BASED REASONING

Representing biomedical knowledge is an essential task in biomedical informatics intelligent systems. Case-based reasoning (CBR) holds the promise to represent contextual knowledge in a way that was not possible before with traditional knowledge-based methods. One main issue in biomedical CBR is dealing with the rate of generation of new knowledge in biomedical fields, which often makes the content of a case base partially obsolete. This article proposes to make use of the concept of prototypical case to ensure that a CBR system would keep update with current research advances in the biomedical field. Prototypical cases have served various purposes in biomedical CBR systems, among which to organize and structure the memory, to guide the retrieval as well as the reuse of cases, and to serve as bootstrapping a CBR system memory when real cases are not available in sufficient quantity and/or quality. This paper emphasizes the different roles prototypical cases can play in CBR systems, and presents knowledge maintenance as a very important novel role for these prototypical cases.     

MEMORY ORGANIZATION AS THE MISSING LINK BETWEEN CASE-BASED REASONING AND INFORMATION RETRIEVAL IN BIOMEDICINE

Mémoire proposes a general framework for reasoning from cases in biology and medicine. Part of this project is to propose a memory organization capable of handling large cases and case bases as occur in biomedical domains. This article presents the essential principles for an efficient memory organization based on pertinent work in information retrieval (IR). IR systems have been able to scale up to terabytes of data taking advantage of large databases research to build Internet search engines. They search for pertinent documents to answer a query using term-based ranking and/or global ranking schemes. Similarly, case-based reasoning (CBR) systems search for pertinent cases using a scoring function for ranking the cases. Mémoire proposes a memory organization based on inverted indexes which may be powered by databases to search and rank efficiently through large case bases. It can be seen as a first step toward large-scale CBR systems, and in addition provides a framework for tight cooperation between CBR and IR.     

基于序关系的非完备信息案例推理集成方法研究

针对案例推理系统中数据集存在数据缺失的非完备信息问题,利用序关系基本原理,设计了案例推理集成方法(ORCBR)。通过对非完备信息下确定符号属性、确定数值属性、区间数值属性以及模糊语言属性等属性间相似性度量的研究,计算出目标案例与历史案例的相似性矩阵。在此基础上,利用序关系构建了相似性矩阵中不同属性的集成排序算法,从而得到最相似历史案例。通过对UCI数据库中非完备信息数据集的测试表明,OR-CBR方法比经典案例推理方法准确率高、效率高,很好地解决了非完备信息数据集的案例推理问题。