Case-based reasoning is a methodology not a technology

Neural Computing & Applications, 2001

In this survey paper, the-state-of-art of the connectionist model (i.e. Artificial Neural Network (ANN)) based methodology for a Case-Based Reasoning (CBR) system design is discussed. Special emphasis is laid on how the ANN can advance CBR technology by building an ANN-based CBR system, or integrating itself as a component within a CBR system. Several ANN models proposed for constructing a CBR system and for solving some special issues involved in a CBR process are described. The main characteristics of each model are analysed, and the advantages and limitations of different models are compared. Also, future research directions are outlined.

Knowledge-Based Systems, 1996

A model to implement the case-based reasoning developed from using a neural net as a base to calculate a measure of similarity between the new problem and each case is presented. It is shown how the neural net provides a mechanism to retrieve cases considering information that in other models needs a parallel architecture. Moreover, in this paper it is analyzed how the model can be employed to create expert networks and case-based planners. ~ 1997 Elsevier Science B.V. All rights reserved.

The article discusses some applications of fuzzy logic ideas to formalizing of the Case-Based Reasoning (CBR) process and to measuring the effectiveness of CBR systems

Journal of Electrical Engineering

Case-based reasoning is a relatively new and promising area of artificial intelligence. In general speaking, case-based reasoning (CBR) is a reasoning method that facilitates knowledge management in which knowledge is a case base acquired by a learning process. Case-based reasoning can be used, for solving problems, in many practical domains such as: mechanical engineering, medicine, business administration etc. Furthermore, for each domain, various task types can be implemented. Some of them are: classification, diagnosis, configuration, planning, decision support etc. The purpose of this paper is to present basic concepts of this promising area. At the beginning, some technical terms of artificial intelligence are introduced. Following this, the foundations of case-based reasoning are presented. At the end, Case Retrieval Netan efficient memory structure for implementation case-based reasoning systems, is described.

Ai Communications, 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. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

2009

Case-based reasoning (CBR) is a sub-field of Artificial Intelligence that deals with experience-based problem solving. CBR has its roots in different disciplines such as cognitive science, machine learning, and knowledge-based systems. Today, it is a well established research field of its own, which produced a rich variety of specific methods, as well as applications implementing those methods for particular tasks and domains. This paper gives a compact overview of CBR in general and further discusses recent advancements in selected topics.

Abstract The use of database technologies for implementing CBR techniques is attracting a lot of attention for several reasons. First, the possibility of using standard DBMS for storing and representing cases significantly reduces the effort needed to develop a CBR system; in fact, data of interest are usually already stored into relational databases and used for different purposes as well.

1997

This paper gives a survey about existing case-based reasoning systems and what can be learned from their development and usage. The information gathering for this survey has been carried out by sending two structured questionnaires to developers of case-based reasoning systems. On the basis of 50 answers coming from all over the world we classify and summarise the results concerning the cases acquired and used, the tasks fulfilled, the background knowledge needed and interesting experiences from bringing the systems into use. The information gathered enables us to identify those steps in the CBR cycle [AaPl94] that require further research and development. Finally we give an outlook to the next step currently underway that is directed towards making the collected CBR cases available in a CBR system that is accessible via WWW. By means of this 'meta CBR system' we give the CBR community an opportunity to learn from experience through using the CBR technology for the improvement of our own field.

2008

Case-Based Reasoning (CBR) is a relatively new and promising technique of artificial intelligence. Using CBR, every new problem is solved by adapting the solutions of the previously successfully solved similar problems. The intention of our research is to develop a robust and general framework which supports generation of wide-range of CBR systems using different approaches. Presented framework integrates two previously developed CBR shells: CaBaGe and CuBaGe. CaBaGe (Case Base Generator) is a CBR shell for generating arbitrary decision support systems where the cases and the problems are represented as a set of values of some selected, most important attributes. CuBaGe (Curve Base Generator) is also a CBR shell in which both the problem and the previous cases are presented in the graphical manner using curves or time-series. Presented framework, which encompasses these two shells, inherits a number of advantages including: domain independence, incremental learning, platform independence, fast retrieval algorithm, generality, and robustness.

International Journal of Hybrid Intelligent Systems, 2009

Case-based reasoning is a popular approach used in intelligent systems. Whenever a new case has to be dealt with, the most similar cases are retrieved from the case base and their encompassed knowledge is exploited in the current situation. Combinations of case-based reasoning with other intelligent methods have been explored deriving effective knowledge representation schemes. Although some types of combinations have been mostly explored, other types have not been thoroughly investigated. In this paper, we briefly outline popular case-based reasoning combinations. More specifically, we focus on combinations of case-based reasoning with rulebased reasoning, soft computing and ontologies. We illustrate basic types of such combinations and discuss future directions.