案例推理分类器属性权重的内省学习调整方法

针对案例推理（CBR）分类器中案例属性权重的分配问题,提出一种基于内省学习的属性权重迭代调整方法。该方法可根据CBR分类器对训练案例分类的结果调整属性的权重。基于成功驱动的权重学习策略,若当前训练案例分类成功,则首先根据权重调整公式增加匹配属性的权重并减少不匹配属性的权重;然后对所有权重进行归一化从而得到当次迭代的新权重。实验结果表明,所提方法的CBR分类器在UCI数据集PD、Heart和WDBC的准确率比传统CBR分类器分别提高1.72%、4.44%和1.05%。故成功驱动的内省学习权重调整方法可以提高权重分配的合理性,进而提高CBR分类器的准确率。     

Intelligent index selection for case-based reasoning

In this paper, we present an indexing technique for case-based reasoning called D-HS^E, that is shown to be more competent than and twice as efficient as the commonly used R-tree. D-HS^E was designed to addresses periodical competency shortcomings of the related D-HS^M index but unfortunately in doing so some efficiency was seen to be sacrificed. In order to address this problem of competency verses efficiency, we propose an intelligent selection algorithm that automatically analyses the case-base and decides which index (D-HS^M or D-HS^E) should be used to optimize performance. The algorithm is designed to favour competency at the expense of efficiency where a competency gain is deemed highly likely to be achieved by using the less efficient approach. In effect we are proposing a flexible indexing scheme that is aware of changes within its environment and which reacts to these changes to optimize performance.     

基于事例推理中差异驱动的事例修改策略研究

总结CBR已有的事例修改的理论和方法,以知识的观点重新看待事例修改问题,从而找到了事例修改的难点,并在此基础上提出了在CBR体系结构上加入知识库,用来存储事例修改的规则;采用差异驱动的事例修改策略获得事例修改规则并进行事例修改。不仅充分利用了已有的CBR流程和事例,并对采用特征和特征值表示的事例具有普遍的意义。     

基于实例推理系统中的权重分析

指标权重的确定在基于实例推理(CBR)系统的检索模型中起着重要的作用。采用基于多位专家的二级模糊综合评判方法求得各个指标的总的综合权重,对指标权重进行了讨论,并引入关联度的概念,讨论了各专家的偏离度及一致性。实例证明有效、可行。     

基于CBR和XML的软构件检索方法

在对现有构件检索方法分析的基础上,探讨了一种基于案例推理和XML技术的智能化软件构件的检索框架。重点阐述了构件案例库中构件的XML知识表示方法以及构件检索中需求构件和案例库中构件的相似度评估方法,提出了一种计算案例相似度的递归算法。     

A use of case-based reasoning technique in building expert systems

Matra Marconi Space France and Aramiihs (Action de Recherche et Application Matra Irit en Interaction Homme Système) laboratory have used and evaluated Case Based Reasoning (CBR) techniques in two projects:

• - The first project is about the development of a system dedicated to help satellites AIT/AIV (Assembly Integration and Test/Validation) test engineers to cope with incidents occurring during test activities. The project is funded by the EGSE System Section of ESTEC (European Space Research and Technology Centre.).

• - The second project is related to the building of a knowledge-based system for diagnosis assistance in AIT/AIV activities of Ariane4 Vehicle Equipment Bay (VEB). The project is financed by internal funding of MMS-F.

In the two projects, CBR technique is neither used the same way nor with the same purpose.

In the first project, CBR technique is used to find out or suggest the cause of an anomaly when an incident appears. Confronted with the occurrence of an incident, the system will refer to its characteristics (test context, symptoms…) that are considered as relevant to retrieve previous similar incidents.

In the second project, CBR technique is combined with Rule Based Reasoning and Model Based Reasoning ones to form the reasoning core of a Hybrid Knowledge Based System. When an incident occurs, the system proposes to test engineers a diagnosis approach based on the combination of different knowledge (coded into rule, cases and models).

Aramiihs is a research unit where engineers from MMS and researchers from the IRIT (Institut de Recherche en Informatique de Toulouse) CNRS (Centre National de la Recherche Scientifique) collaborate on problems concerning new types of man-system interaction.     

案例推理分类器的权重分配及案例库维护方法

由于特征权重分配以及案例库维护对案例推理(CBR)分类器的性能有重要影响,提出了用蚁狮(ALO)算法来分配权重且用高斯混合模型的期望最大化算法(GMMEM)进行案例库维护的案例推理算法模型——AGECBR(Ant Lion and Expectation Maximization of Gaussian Mixture...     

Scale-guided object matching for case-based reasoning

Case-Based Reasoning (CBR) can be seen as a problem-solving paradigm that advocates the use of previous experiences to limit search spaces and to reduce opportunities for error repetition. In this paradigm, the case at hand is compared against former experiences to select from a set of possible courses of action the best one. A comparison method is required to ensure that the most resembling experience is, in fact, chosen to drive the problem-solving process. This paper discusses an object-oriented framework that provides a scale-guided measure of similarity between objects, and shows how this framework can be applied for case-based reasoning, drawing examples from device diagnosis.     

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     

Achieving quality assurance functionality in the food industry using a hybrid case-based reasoning and fuzzy logic approach

Quality control of food inventories in the warehouse is complex as well as challenging due to the fact that food can easily deteriorate. Currently, this difficult storage problem is managed mostly by using a human dependent quality assurance and decision making process. This has however, occasionally led to unimaginative, arduous and inconsistent decisions due to the injection of subjective human intervention into the process. Therefore, it could be said that current practice is not powerful enough to support high-quality inventory management. In this paper, the development of an integrative prototype decision support system, namely, Intelligent Food Quality Assurance System (IFQAS) is described which will assist the process by automating the human based decision making process in the quality control of food storage. The system, which is composed of a Case-based Reasoning (CBR) engine and a Fuzzy rule-based Reasoning (FBR) engine, starts with the receipt of incoming food inventory. With the CBR engine, certain quality assurance operations can be suggested based on the attributes of the food received. Further of this, the FBR engine can make suggestions on the optimal storage conditions of inventory by systematically evaluating the food conditions when the food is receiving. With the assistance of the system, a holistic monitoring in quality control of the receiving operations and the storage conditions of the food in the warehouse can be performed. It provides consistent and systematic Quality Assurance Guidelines for quality control which leads to improvement in the level of customer satisfaction and minimization of the defective rate.     

An ontology in OWL for legal case-based reasoning

The paper gives ontologies in the Web Ontology Language (OWL) for Legal Case-based Reasoning (LCBR) systems, giving explicit, formal, and general specifications of a conceptualisation LCBR. Ontologies for different systems allows comparison and contrast between them. OWL ontologies are standardised, machine-readable formats that support automated processing with Semantic Web applications. Intermediate concepts, concepts between base-level concepts and higher level concepts, are central in LCBR. The main issues and their relevance to ontological reasoning and to LCBR are discussed. Two LCBR systems (AS-CATO, which is based on CATO, and IBP) are analysed in terms of basic and intermediate concepts. Central components of the OWL ontologies for these systems are presented, pointing out differences and similarities. The main novelty of the paper is the ontological analysis and representation in OWL of LCBR systems. The paper also emphasises the important issues concerning the representation and reasoning of intermediate concepts.

Introducing attribute risk for retrieval in case-based reasoning

One of the major assumptions in case-based reasoning is that similar experiences can guide future reasoning, problem solving and learning. This assumption shows the importance of the method used for choosing the most suitable case, especially when dealing with the class of problems in which risk, is relevant concept to the case retrieval process. This paper argues that traditional similarity assessment methods are not sufficient to obtain the best case; an additional step with new information must be performed necessary, after applying similarity measures in the retrieval stage. When a case is recovered from the case base, one must take into account not only the specific value of the attribute but also whether the case solution is suitable for solving the problem, depending on the risk produced in the final decision. We introduce this risk, as new information through a new concept called risk information that is entirely different from the weight of the attributes. Our article presents this concept locally and measures it for each attribute independently.     

Mining competent case bases for case-based reasoning

Case-based reasoning relies heavily on the availability of a highly competent case base to make high-quality decisions. However, good case bases are difficult to come by. In this paper, we present a novel algorithm for automatically mining a high-quality case base from a raw case set that can preserve and sometimes even improve the competence of case-based reasoning. In this paper, we analyze two major problems in previous case-mining algorithms. The first problem is caused by noisy cases such that the nearest neighbor cases of a problem may not provide correct solutions. The second problem is caused by uneven case distribution, such that similar problems may have dissimilar solutions. To solve these problems, we develop a theoretical framework for the error bound in case-based reasoning, and propose a novel case-base mining algorithm guided by the theoretical results that returns a high-quality case base from raw data efficiently. We support our theory and algorithm with extensive empirical evaluation using different benchmark data sets.     

Synergistic case-based reasoning in medical domains

This paper presents four synergistic systems that exemplify the approaches and benefits of case-based reasoning in medical domains. It then explores how these systems couple Artificial Intelligence (AI) research with medical research and practice, integrate multiple AI and computing methodologies, leverage small numbers of available cases, reason with time series data, and integrate numeric data with contextual and subjective information. The following systems are presented: (1) CARE-PARTNER, which supports the long-term follow-up care of stem-cell transplantation patients; (2) the 4 Diabetes Support System, which aids in managing patients with type 1 diabetes on insulin pump therapy; (3) Retrieval of HEmodialysis in NEphrological Disorders, which supports hemodialysis treatment of patients with end stage renal disease; and (4) the Mälardalen Stress System, which aids in the diagnosis and treatment of stress-related disorders.     

Learning fuzzy rules for similarity assessment in case-based reasoning

Fundamental to case-based reasoning is the assumption that similar problems have similar solutions. The meaning of the concept of “similarity” can vary in different situations and remains an issue. This paper proposes a novel similarity model consisting of fuzzy rules to represent the semantics and evaluation criteria for similarity. We believe that fuzzy if-then rules present a more powerful and flexible means to capture domain knowledge for utility oriented similarity modeling than traditional similarity measures based on feature weighting. Fuzzy rule-based reasoning is utilized as a case matching mechanism to determine whether and to which extent a known case in the case library is similar to a given problem in query. Further, we explain that such fuzzy rules for similarity assessment can be learned from the case library using genetic algorithms. The key to this is pair-wise comparisons of cases with known solutions in the case library such that sufficient training samples can be derived for genetic-based fuzzy rule learning. The evaluations conducted have shown the superiority of the proposed method in similarity modeling over traditional schemes as well as the feasibility of learning fuzzy similarity rules from a rather small case base while still yielding competent system performance.     

Real-time retrieval for case-based reasoning in interactive multiagent-based simulations

The aim of this paper is to present the principles and results about case-based reasoning adapted to real-time interactive simulations, more precisely concerning retrieval mechanisms. The article begins by introducing the constraints involved in interactive multiagent-based simulations. The second section presents a framework stemming from case-based reasoning by autonomous agents. Each agent uses a case base of local situations and, from this base, it can choose an action in order to interact with other autonomous agents or users’ avatars. We illustrate this framework with an example dedicated to the study of dynamic situations in football. We then go on to address the difficulties of conducting such simulations in real-time and propose a model for case and for case base. Using generic agents and adequate case base structure associated with a dedicated recall algorithm, we improve retrieval performance under time pressure compared to classic CBR techniques. We present some results relating to the performance of this solution. The article concludes by outlining future development of our project.     

Data mining for case-based reasoning in high-dimensional biological domains

Case-based reasoning (CBR) is a suitable paradigm for class discovery in molecular biology, where the rules that define the domain knowledge are difficult to obtain and the number and the complexity of the rules affecting the problem are too large for formal knowledge representation. To extend the capabilities of CBR, we propose the mixture of experts for case-based reasoning (MOE4CBR), a method that combines an ensemble of CBR classifiers with spectral clustering and logistic regression. Our approach not only achieves higher prediction accuracy, but also leads to the selection of a subset of features that have meaningful relationships with their class labels. We evaluate MOE4CBR by applying the method to a CBR system called TA3 - a computational framework for CBR systems. For two ovarian mass spectrometry data sets, the prediction accuracy improves from 80 percent to 93 percent and from 90 percent to 98.4 percent, respectively. We also apply the method to leukemia and lung microarray data sets with prediction accuracy improving from 65 percent to 74 percent and from 60 percent to 70 percent, respectively. Finally, we compare our list of discovered biomarkers with the lists of selected biomarkers from other studies for the mass spectrometry data sets.     

A case-based reasoning system for identifying failure mechanisms

Correctly identifying the mechanism responsible for a failure is a major step in failure analysis. Today, human experts normally perform this task. In the problem-solving process, human experts often recall similar cases to help identifying the mechanism involved. This has motivated the use of case-based reasoning to develop a computerized system for failure-mechanism identification in this study. Major issues and the methods applied are discussed. To determine its accuracy, the system is subsequently evaluated using historical cases, which are classified into two categories: standard and exceptional. The test results show that 100% accuracy can be achieved for standard cases, and that exceptional cases also attain accuracy as high as 71.25%. It is thus concluded that case-based reasoning is a viable approach for the identification of failure mechanisms.