A Case for Usage of Case Usage in Case-Based Reasoning

1993

When we use Case-Based Reasoning (CBR) for practical applications, it is often the case to implement two kinds of problem solvers: casebased one and knowledge-based/conventional one. The complex integration of such plural reasoners often causes serious VV~T problems: performance of problem solving, quality of solutions, and so on. In this paper, we ~ldress the performance validation problems of a CBR system. This paper presents a blackbox validation approach to quantitatively analyze the performance and proposes three kinds of performance measures. The proposed measures are then applied to validating a CBR system: IRS-CHR (Intelligent Information Retriever with a Case-Based Reasoner). From the experimental results, we conclude that (1) IRS-CBR has succeeded in both speed-up and memory-based learning, and (2) the proposed measures are useful for validating a CBR system.

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1997

Case-based reasoning (CBR) supports ill-structured decision making by retrieving previous cases that are useful toward the solution of a new decision problem. The usefulness of previous cases is determined by assessing the similarity of a new case with the previous cases. In this paper, we present a modified form of the cosine matching function that makes it possible to contrast the two cases being matched and to include differences in the importance of features in the new case and the importance of features in the previous case. Our empirical evaluation of a CBR application to a diagnosis and repair task in an electromechanical domain shows that the proposed modified cosine matching function has a superior retrieval performance when compared to the performance of nearest-neighbor and the Tversky's contrast matching functions. Recent CBR systems mainly retrieve previous cases to offer decision support [1], . The retrieval of relevant previous cases is critical to their success . Central to retrieval methodologies is the assessment of the similarity of a new others.

1995

Evaluation is an important issue for every scientific field and a necessity for an emerging software technology like case-based reasoning. This paper is a supplementation to the review of industrial case-based reasoning tools by K.-D. Althoff, E. Auriol, R. Barletta and M. Manago which describes the most detailed evaluation of commercial case-based reasoning tools currently available. The author focuses on some important aspects that correspond to the evaluation of case-based reasoning systems and gives links to ongoing research.

1995

Case-based reasoning is a recent approach to knowledge-based problem solving and decision support: A new problem is solved by remembering a previous similar situation and by reusing information and knowledge of that situation. Let us illustrate this by looking at some typical problem solving situations:

1995

Knowledge-Based Systems, 1999

This paper asks whether case-based reasoning is an artificial intelligence (AI) technology like rule-based reasoning, neural networks or genetic algorithms or whether it is better described as a methodology for problem solving, that may use any appropriate technology. By describing four applications of case-based reasoning (CBR), that variously use: nearest neighbour, induction, fuzzy logic and SQL, the author shows that CBR is a methodology and not a technology. The implications of this are discussed. ᭧

2001

This paper aims at helping to decide whether case-based reasoning (CBR) is an appropriate problem-solving approach for particular business application needs.

IEE Colloquium on Case Based Reasoning: Prospects for Applications

This paper is about work in progress on the application of Case Based Reasoning (CBR) to problems of Information Retrieval (IR). Many approaches to IR have already been developed. They inv olve techniques such as keyword searches and the use of boolean queries which are applied to databases or indexes. Other methods are based on probabilistic or statistical analysis of occurrences of words or phrases, some on structured queries, and yet others work with vector space models or semantic models. Knowledgebased approaches are not uncommon. For example, expert intermediary systems may be used to formulating and evaluating queries. However, there is little experimental evidence to demonstrate the effectiveness of knowledge-based techniques and they remain a significant challenge for research . The main idea under Case-Based Reasoning (CBR) is to store experience as cases, to store problem-solving processes as instances of cases. When a new problem is encountered, the system uses its memory of relevant past cases to interpret or solve the problem. Adaptation of cases plays an important role in this process. Thus, the more cases the system stores the better it performs. CBR allows exceptional cases to be stored and used in problem solving, makes learning from cases possible, and provides more convincing explanations, based upon the stored cases. Moreover, cases can be used to establish general rules. Due to this characteristics, CBR is specially useful in areas that (a) are particularly difficult to formalise (b) planning and explanations are required (c) persuasion is important, or (d) hypothetical scenarios are needed. Learning and adaptation have long been viewed as crucial part of an IR system , and this alone suggests that CBR might prove to be a valuable approach to this area. In this paper, a CBR model is proposed as an alternative approach to to IR. The possible relationships of a CBR system to the various facets of current human and automated systems are considered, with particular attention being paid to classificatory schemes and keyword searches. However, the focus of the paper is on issues of representation and adaptation. Close attention is paid to what exactly should constitute a case; and a system is described which starts from a problem description, ie a need for information, and a context, which involves both intellectual and institutional features. Learning takes place by adding new cases and modifying old cases in the system's case-base.

1994

Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe as well.