Case-based reasoning solves new problems by re-using the solutions of previously solved similar problems and is popular because many of the knowledge engineering demands of conventional knowledge-based systems are removed. The content of the case knowledge container is critical to the performance of case-based classification systems. However, the knowledge engineer is given little support in the selection of suitable techniques to maintain and monitor the case base. This research investigates the coverage, competence and problem-solving capacity of case knowledge with the aim of developing techniques to model and maintain the case base. We present a novel technique that creates a model of the case base by measuring the uncertainty in local areas of the problem space based on the local mix of solutions present. The model provides an insight into the structure of a case base by means of a complexity profile that can assist maintenance decision-making and provide a benchmark to assess future changes to the case base. The distribution of cases in the case base is critical to the performance of a case-based reasoning system. We argue that classification boundaries represent important regions of the problem space and develop two complexity-guided algorithms which use boundary identification techniques to actively discover cases close to boundaries. We introduce a complexity-guided redundancy reduction algorithm which uses a case complexity threshold to retain cases close to boundaries and delete cases that form single class clusters. The algorithm offers control over the balance between maintaining competence and reducing case base size. The performance of a case-based reasoning system relies on the integrity of its case base but in real life applications the available data invariably contains erroneous, noisy cases. Automated removal of these noisy cases can improve system accuracy. In addition, error rates can often be reduced by removing cases to give smoother decision boundaries between classes. We show that the optimal level of boundary smoothing is domain dependent and, therefore, our approach to error reduction reacts to the characteristics of the domain by setting an appropriate level of smoothing. We introduce a novel algorithm which identifies and removes both noisy and boundary cases with the aid of a local distance ratio. A prototype interface has been developed that shows how the modelling and maintenance approaches can be used in practice in an interactive manner. The interface allows the knowledge engineer to make informed maintenance choices without the need for extensive evaluation effort while, at the same time, retaining control over the process. One of the strengths of our approach is in applying a consistent, integrated method to case base maintenance to provide a transparent process that gives a degree of explanation.

The purpose of this study was to examine the effectiveness of cased-based learning, writing and peer discussions on learning about digestive diseases in a computer-based learning environment, BioWorld. This method was called the Grand Rounds method. Thirty-one, ninth grade biology students participated in the study. Two classes were randomly selected as the Rounds group and the No Rounds group. All students worked collaboratively in pairs to solve diagnostic problems on BioWorld. The Rounds group then engaged in the Grand Rounds activities while the No Rounds group conducted a web search and solved a final BioWorld problem. Both treatments demonstrated significant knowledge gains of digestive problems from pretest to posttest but the gains were greater in the Rounds group. There were no significant changes from pre to post questionnaire in students' attitudes towards biology or peer work/discussion. The verbal protocols revealed students used diagnostic heuristics while solving cases, and discourse communities emerged among the students. Overall, this study confirms the benefits of written and oral discourse, and authentic learning activities in classrooms.

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Case-Based Reasoning (CBR) is a technique in Artificial Intelligence where a new problem is solved by making use of the solution to a similar past problem situation. People naturally solve problems in this way, without even thinking about it. For example, an occupational therapist (OT) that assesses the needs of a new disabled person may be reminded of a previous person in terms of their disabilities. He may or may not decide to recommend the same devices based on the outcome of an earlier (disabled) person. Case-based reasoning makes use of a collection of past problem-solving experiences thus enabling users to exploit the information of others’ successes and failures to solve their own problem(s). This project has developed a CBR tool to assist in matching SmartHouse technology to the needs of the elderly and people with disabilities. The tool makes suggestions of SmartHouse devices that could assist with given impairments. SmartHouse past problem-solving textual reports have been used to obtain knowledge for the CBR system. Creating a case-based reasoning system from textual sources is challenging because it requires that the text be interpreted in a meaningful way in order to create cases that are effective in problem-solving and to be able to reasonably interpret queries. Effective case retrieval and query interpretation is only possible if a domain-specific conceptual model is available and if the different meanings that a word can take can be recognised in the text. Approaches based on methods in information retrieval require large amounts of data and typically result in knowledge-poor representations. The costs become prohibitive if an expert is engaged to manually craft cases or hand tag documents for learning. Furthermore, hierarchically structured case representations are preferred to flat-structured ones for problem-solving because they allow for comparison at different levels of specificity thus resulting in more effective retrieval than flat structured cases. This project has developed SmartCAT-T, a tool that creates knowledge-rich hierarchically structured cases from semi-structured textual reports. SmartCAT-T highlights important phrases in the textual SmartHouse problem-solving reports and uses the phrases to create a conceptual model of the domain. The model then becomes a standard structure onto which each semi-structured SmartHouse report is mapped in order to obtain the correspondingly structured case. SmartCAT-T also relies on an unsupervised methodology that recognises word synonyms in text. The methodology is used to create a uniform vocabulary for the textual reports and the resulting harmonised text is used to create the standard conceptual model of the domain. The technique is also employed in query interpretation during problem solving. SmartCAT-T does not require large sets of tagged data for learning, and the concepts in the conceptual model are interpretable, allowing for expert refinement of knowledge. Evaluation results show that the created cases contain knowledge that is useful for problem solving. An improvement in results is also observed when the text and queries are harmonised. A further evaluation highlights a high potential for the techniques developed in this research to be useful in domains other than SmartHouse. All this has been implemented in the Smarter case-based reasoning system.

Case-Based Reasoning is a fairly new Artificial Intelligence technique which makes use of past experience as the basis for solving new problems. Typically, a case-based reasoning system stores actual past problems and solutions in memory as cases. Due to its ability to reason from actual experience and to save solved problems and thus learn automatically, case-based reasoning has been found to be applicable to domains for which techniques such as rule-based reasoning have traditionally not been well-suited, such as experience-rich, unstructured domains. This applicability has led to it becoming a viable new artificial intelligence topic from both a research and application perspective. This dissertation concentrates on researching and implementing indexing techniques for casebased reasoning. Case representation is researched as a requirement for implementation of indexing techniques, and pre-transportation decision making for hazardous waste handling is used as the domain for applying and testing the techniques. The field of case-based reasoning was covered in general. Case representation and indexing were researched in detail. A single case representation scheme was designed and implemented. Five indexing techniques were designed, implemented and tested. Their effectiveness is assessed in relation to each other, to other reasoners and implications for their use as the basis for a case-based reasoning intelligent decision support system for pre-transportation decision making for hazardous waste handling are briefly assessed. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 1997.

This thesis focuses on the application of case based reasoning (CBR) to welding fixtures in a computer aided design (CAD) environment. Modular fixtures have become more popular in previous years due to the creation of flexible manufacturing systems. Modular fixtures, since they are composed of many standardized parts, require much iteration to produce a full fixture design. This process is made more complicated when it is applied to more complex parts such as welding assemblies. In an effort to simplify fixture design for such complicated parts, researchers have been working on integrating fixture design into CAD packages. These efforts, generally known as computer aided fixture design (CAFD), do not focus on the transition of experience from more experienced designers but only provide a structure and a virtual environment to create fixtures. The research presented in this thesis will apply to this area. Case based reasoning (CBR) is a method of using previous cases to help aid the development of solutions to new problems. Applied to CAFD, this method is reduced to the application of a database and a retrieval and adaptation system. Current research on CAFD and CBR is limited to only proposing systems for machining fixtures. This thesis presents a methodology of a CAFD and CBR system that is dedicated to welding assemblies and fixtures. The focus is on creating an indexing system that adequately represents the workpiece and fixture, a retrieval system that accurately recovers the previous cases, and a method that integrates designer feedback in each process. The results of this thesis will be shown in a case study using an automobile muffler fixture assembly to define each idea of the methodology and to provide an example.

Case-Based Reasoning (CBR) is an appealing technique for developing intelligent systems. Besides its psycho- logical plausibility and a substantial body of research during recent years, building a good CBR system remains still a difficult task. The main problems remaining are the development of suitable case retrieval and adaptation mechanisms for CBR. The major issues are how and when to capture the necessary knowledge for both of the above aspects. As a contribution to knowledge this thesis proposes a new approach to address the experienced difficulties. The basic framework of Ripple Down Rules (RDR) is extended to allow the incremental development of a knowledge base for each of the two functions: case retrieval and case adaptation, during the use of the system while solving actual problems. The proposed approach allows an expert-user to provide explanations of why, for a given problem, certain actions should be taken. Incrementally knowledge is acquired from the expert-user in which the expert refines a rule which performs unsatisfactorily for a current given problem. The approach facilitates both, the rule acquisition as well as its validation. As a result the knowledge maintenance task of a knowledge engineer is overcome. This approach is effective with respect to both, the development of highly tailored and complex retrieval and adaptation functions for CBR as well as the provision of an intuitive and feasible approach for the expert. The approach has been implemented in a CBR system named MIKAS (Menu Construction using Incre- mental Knowledge Acquisition Systems) for the design of menus (diets) according to dietary requirements. The experimental evidence indicates the suitability of the approach to address the retrieval and adaptation problems of the menu construction domain. The experimental evidence also indicates that the difficulties of developing retrieval and adaptation functions for CBR can be effectively overcome by the proposed new approach. It is expected that the approach is likely to be useful in other problem solving domains where expert intervention is Required to modify a solution.

Thesis (Doctoral)--Universität Osnabrücks, 2006. / Includes abstract and bibliographical references (p. 216-231).

As a technique to solve new problems based on previous successful cases, CBR represents significant prospects for improving the accuracy and effectiveness of unstructured decision-making problems. Similar problems have similar solutions is the main assumption. Utility oriented similarity modeling is gradually becoming an important direction for Case-based reasoning research. In this thesis, we propose a new way to represent the utility of case by using fuzzy rules. Our method could be considered as a new way to estimate case utility based on fuzzy rule based reasoning. We use modified WANG’s algorithm to generate a fuzzy if-then rule from a case pair instead of a single case. The fuzzy if-then rules have been identified as a powerful means to capture domain information for case utility approximation than traditional similarity measures based on feature weighting. The reason why we choose the WANG algorithm as the foundation is that it is a simpler and faster algorithm to generate if-then rules from examples. The generated fuzzy rules are utilized as a case matching mechanism to estimate the utility of the cases for a given problem. The given problem will be formed with each case in the case library into pairs which are treated as the inputs of fuzzy rules to determine whether or to which extent a known case is useful to the problem. One case has an estimated utility score to the given problem to help our system to make decision. The experiments on several data sets have showed the superiority of our method over traditional schemes, as well as the feasibility of learning fuzzy if-then rules from a small number of cases while still having good performances.

The procedure for developing a new product, in general, is as follows. First, the design engineer must have a thorough understanding regarding the encountered problem. And, he must produce some design concepts based on the perceived requirements. Finally, some solutions are then achieved according to the prescribed design concepts. Unfortunately, few researchers have been able to explain, in a specific rather than abstract manner, the process of generating pertinent design concepts. However, this process has to be a very critical link in the chain. Without obtaining a good design concept the entire design procedure will fall, not to mention to find a suitable solution. In this research we use an interesting analogy between the design procedure and the well-familiarized Sun/Water-cycle system, to concretely describe the task of inspiration of innovative concepts particularly in engineering design. The use of this analogy, we believe, will guide engineers to more effectively and more efficiently go through the stages of conceptual design. Consequently, the entire product development time can be reduced.