Components for Case-Based Reasoning Systems

Case-Based Reasoning (CBR) is a problem-solving paradigm that solves a new problem by remembering a previous similar situation and by reusing the information and knowledge of that situation. A Case-Based Reasoning (CBR) tool is software that can be used to develop several applications that require case-based reasoning methodology. CBR is based on the intuition that information gained from previous experiences (cases or instances) can be an important tool to provide solutions for, and enhance processes related to, the problem at hand. Therefore, it helps improve outcomes and saving valuable resources. However, large volumes of information can make it a complex task to gain useful insight from historic datasets. CBR is an effective tool to gain useful information in such circumstances and is one of the most successfully applied Artificial Intelligence technologies in recent years. This paper gives background information about CBR software and introduces the most used CBR tools (CBR Shell, FreeCBR, jCOLIBRI, myCBR and eXiTCBR). Then it introduces a comparative analysis study based on some determined factors that affect the CBR software including noisy data or missing values in the cases. It also compares between them from the? Finally, an evaluation of the retrieving phase of each software is introduced.

Expert Systems With Applications, 1998

Most of the tools for the construction of Knowledge Based Systems (KBSs) offer a reasoning mechanism within the logical scheme, some of them combining rules with objects. At the end of the 1970s to the beginning of the 1980s cases began to be used as an alternative to representing and storing experience and knowledge of organizations. This is how Case-Based Reasoning (CBR) systems, which are capable of solving problems using solutions of previously solved problems stored in the Case Memory (CM), were originated. In this article, we introduce RBCShell ~, a tool to construct KBSs with CBR. We also present an application developed by means of RBCShelI.

This paper presents the comparison, in depth, of the capabilities of two Case-Based Reasoning (CBR) oriented software frameworks myCBR and jCOLIBRI for the development of predictive diagnosis and maintenance systems. Those frameworks were selected after detailed preliminary investigations [1] of the most popular noncommercial software systems for CBR as: myCBR, jCOLIBRI, CBR*Tools, CAT-CBR, CASPIAN. The evaluation of myCBR and jCOLIBRI includes the capacity to support the: R 4 CBR circle; clusterization of cases, variety of used similarity functions, etc. Specific abilities to provide GUI, database support, required knowledge to work with the systems were also considered.

Lecture Notes in Computer Science, 2003

Case-based reasoning means learning from previous experiences. Given the fact that this is a very general approach to human problem-solving behavior, it is more than natural that there are different approaches for implementing this process on computer systems. In commercial CBR systems, there are three main approaches that differ in the sources, materials, and knowledge they use.

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.

Annals of Operations Research, 1997

A case-based reasoning (CBR) system supports decision makers when solving new decision problems (ie, new cases) on the basis of past experience (ie, previous cases). The effectiveness of a CBR system depends on its ability to retrieve useful previous cases. The usefulness ...

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.

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

1997

We compared the INRECA case-based reasoning (CBR) system (Esprit project 6322) with five industrial CBR tools, namely CBR EXPRESS (Inference, USA), ESTEEM (Esteem Software, USA), KATE tools (AcknoSoft, France), REMIND (Cognitive Systems, USA) and S 3-CASE (tecInno, Germany), and twenty CBR-related research prototype systems (developed at the

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.