FLEA-CBR - A Flexible Alternative to the Classic 4R Cycle of Case-Based Reasoning

摘要

This paper introduces FLEA-CBR, an alternative approach for composition of case-based reasoning (CBR) processes. FLEA-CBR extends the original 4R (Retrieve, Reuse, Revise, Retain) CBR cycle with a flexible order of execution of its main steps. Additionally, a number of combinatorial features for a more comprehensive and enhanced composition can be used. FLEA is an acronym for Find, Learn, Explain, Adapt and was initially created to solve the restrictiveness issues of case-based design (CBD) where many existing approaches consist of the retrieval phase only. However, the methodology can be transferred to other CBR domains too, as its flexibility allows for convenient adaptation to the given requirements and constraints. The main advantages of FLEA-CBR over the classic 4R cycle are the ability to combine and activate the main steps in desired or arbitrary order and the use of the explainability feature together with each of the steps as well as a standalone component, providing a deep integration of Explainable AI (XAI) into the CBR cycle. Besides the CBR methods, the methodology was also conceptualized to make use of the currently popular machine learning methods, such as recurrent and convolutional neural networks (RNN, ConvNet) or general adversarial nets (GAN), for all of its steps. It is also compatible with different case representations, such as graph- or attribute-based. Being a template for a distributed software architecture, FLEA-CBR relies on the autonomy of implemented components, making the methodology more stable and suitable for use in modern container-based environments. Along with the detailed description of the methodology, this paper also provides two examples of its usage: for the domain of CBR-based creativity and library service optimization.