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Overview of the 4R CBR Cycle Modifications (Extended Version)

Viktor Eisenstadt; Klaus-Dieter Althoff
In: Robert Jäschke; Matthias Weidlich (Hrsg.). Lernen, Wissen, Daten, Analysen. GI-Workshop-Tage "Lernen, Wissen, Daten, Analysen" (LWDA-2019), Proceedings of the Conference on "Lernen, Wissen, Daten, Analysen", September 30 - October 2, Berlin, Germany, Pages 230-240, CEUR, 2019.


The currently most well-known approach to apply case-based reasoning (CBR) to an application or a concept is to use the 4R cycle that consists of the steps Retrieve, Reuse, Revise, and Retain. Being a widely accepted synonym for CBR, the classic setting of the 4R cycle became a dominant basis for CBR-based systems during the last decade. However, since its introduction in 1994, multiple modifications of the original cycle were developed as well, mostly with the purpose to add additional features that might be useful in specific application situations or to provide an alternative CBR solution that might be suitable for an entire domain and related domains. In this paper, we present an overview of the most notable CBR cycle modifications. We provide a description of the main features for each selected modification, and then compare them using a number of specific criteria. A special emphasis in this work will be put on Explainable AI (XAI) integration as one of the most recent trends in artificial intelligence. This work is an extended version of the ‘Related Work’ section of our ICCBR 2019 conference paper [5] that presents a flexibility-enhanced version of 4R. The main goals of this paper are to provide a researcher with a comfortable overview of the most relevant and interesting CBR cycle modifications and to discuss the future of the 4R CBR cycle.

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