Decision-theoretic case-based reasoning

We describe a decision-theoretic methodology for case-based reasoning in diagnosis and troubleshooting applications. The system utilizes a special-structure Bayesian network to represent diagnostic cases, with nodes representing issues, causes, and symptoms. Dirichlet distributions are assessed at knowledge acquisition time to indicate the strength of relationships between variables. During a diagnosis session, a relevant subnetwork is extracted from a Bayesian-network database that describes a very large number of diagnostic interactions and cases. The constructed network is used to make recommendations regarding possible repairs and additional observations, based on an estimate of expected repair costs. As cases are resolved, observations of issues, causes, symptoms, and the success of repairs are recorded. New variables are added to the database, and the probabilities associated with variables already in the database are updated. In this way, the inferential behavior of system adjusts to the characteristics of the target population of users. We show how these elements work together in a cycle of troubleshooting tasks, and describe some results from a pilot system implementation and deployment     

Continuous case-based reasoning

Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as on-line sensorimotor interaction with the environment, and continuous adaptation and learning during the performance task. This article introduces a new method for continuous case-based reasoning, and discusses its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (self-improving navigation system). The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. The article concludes with a general discussion of case-based reasoning issues addressed by this research.     

From case-based reasoning to traces-based reasoning

CBR is an original AI paradigm based on the adaptation of solutions of past problems in order to solve new similar problems. Hence, a case is a problem with its solution and cases are stored in a case library. The reasoning process follows a cycle that facilitates “learning” from new solved cases. This approach can be also viewed as a lazy learning method when applied for task classification. CBR is applied for various tasks as design, planning, diagnosis, information retrieval, etc. The paper is the occasion to go a step further in reusing past unstructured experience, by considering traces of computer use as experience knowledge containers for situation based problem solving.     

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.     

An introduction to case-based reasoning

Case-based reasoning means using old experiences to understand and solve new problems. In case-based reasoning, a reasoner remembers a previous situation similar to the current one and uses that to solve the new problem. Case-based reasoning can mean adapting old solutions to meet new demands; using old cases to explain new situations; using old cases to critique new solutions; or reasoning from precedents to interpret a new situation (much like lawyers do) or create an equitable solution to a new problem (much like labor mediators do). This paper discusses the processes involved in case-based reasoning and the tasks for which case-based reasoning is useful.This article is excerpted from Case-Based Reasoning by Janet Kolodner, to be published by Morgan-Kaufmann Publishers, Inc. in 1992.This work was partially funded by darpa under Contract No. F49620-88-C-0058 monitored by AFOSR, by NSF under Grant No. IST-8608362, and by ARI under Contract No. MDA-903-86-C-173.     

混合案例推理和规则推理的集成推理框架

在智能决策系统（IDSS）获取知识的推理体系中，案例推理和规则推理有着各自的优点，而混合两者的集成推理可以克服两者的缺点，提高系统的效率和综合推理能力。但是集成推理系统缺乏通用性，延长了开发周期，且不利于规则库和案例库的重用。一种可扩充的集成推理框架为了解决上面的问题而被提出，该框架利用智能决策支持语言Knonit的组件性，对不同的集成方式可方便地扩充相应的集成推理方案，从而快速地搭建IDSS应用；同时规则和案例是作为Knonit广义知识元存在，可以在集成推理框架中复用，另一方面，Knonit的动态特性和可扩充性也对案例库和知识库动态的调整和扩充提供了支持。     

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。     

An exercise in formalising teleological case-based reasoning

This paper takes up Berman and Hafner's (1993) challenge to model legal case-based reasoning not just in terms of factual similarities and differences but also in terms of the values that are at stake. The formal framework of Prakken and Sartor (1998) is applied to examples of case-based reasoning involving values, and a method for formalising such examples is proposed. The method makes it possible to express that a case should be decided in a certain way because that advances certain values. The method also supports the comparison of conflicting precedents in terms of values, and it supports debates on the relevance of distinctions in terms of values.     

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。     

Financial market monitoring by case-based reasoning

This paper shows that case-based reasoning (CBR), an artificial intelligence technique, is a quite efficient tool in monitoring financial market against its possible collapse. For this purpose, daily financial condition indicator (DFCI) monitoring financial market is built on CBR and its performance is compared to DFCI on neural network. This study is empirically done for the Korean financial market.     

Robotic inspection plan optimizationby case-based reasoning

Inspection planning is discussed in a framework where a rich choice of instruments is available and robots can also participate in the inspection process. The problem of constrained plan optimization is exposed, and a solution is suggested that is based on task grouping. After outlining the overall planning process, we give details of the optimization stage where case-based reasoning is applied. Finally, it will be shown how the implemented knowledge-based system can operate as a knowledge server.     

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     

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.     

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.

Block assembly planning in shipbuilding using case-based reasoning

A process planning system using case-based reasoning (CBR) is developed for block assembly in shipbuilding. A block assembly planning problem is modeled as a constraint satisfaction problem where the precedence relations between operations are considered constraints. In order to find similar cases, we propose two similarity coefficients for finding similar cases and for finding similar relations. Due to the limited number of operation types, the process planning system first matches the parts of the problem and those of the case-based on their roles in the assembly, and then it matches the relations related to the matched part–pairs. The parts involved in more operations are considered first. The process planning system is applied to simple examples for verification and comparison. An interface system is also developed for extracting information from CAD model, for preparing data for process planning, and for visually verifying the assembly sequence.     

Artefact generation in second life with case-based reasoning

Launched in 2003, Second Life is a computer-based pseudo-environment accessed via the Internet. Although a number of individuals and companies have developed a presence (lands) in Second Life, there is no appropriate methodology in place for undertaking such developments. While some existing methods have been adapted by users to their individual needs, this paper explores the development of a method for developing lands specifically within Second Life. This method is based on case-based reasoning (CBR) as this method has a number of similarities with Second Life itself. A system was designed based on CBR with some modifications to work in accordance with Second Life. In this paper, the system and its modifications are discussed and its application to the development of space within Second Life is evaluated. From tracking its progress against previous specifications and future activity, an updated version of the CBR web tool component covering the latest changes and improvements in the tool is introduced here.     

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.     

Diabetic patients management exploiting case-based reasoning techniques

In this paper we propose a case-based decision support tool, designed to help physicians in 1st type diabetes therapy revision through the intelligent retrieval of data related to past situations (or 'cases') similar to the current one. A case is defined as a set of variable values (or features) collected during a visit. We defined taxonomy of prototypical patients' conditions, or classes, to which each case should belong. For each input case, the system allows the physician to find similar past cases, both from the same patient and from different ones. We have implemented a two-steps procedure; (1) it finds the classes to which the input case could belong; (2) it lists the most similar cases from these classes, through a nearest neighbor technique, and provides some statistics useful for decision taking. The performance of the system has been tested on a data-base of 147 real cases, collected at the Policlinico S. Matteo Hospital of Pavia. The tool is fully integrated in the web-based architecture of the EU funded Telematic management of Insulin Dependent Diabetes Mellitus (T-IDDM) project.