A Methodological Approach to Model CBR-based Systems

TL;DR

This paper introduces a clear methodological approach for modeling CBR-based systems using abstract and concrete models, aiming to simplify development and promote wider adoption across various fields.

Contribution

It proposes a novel two-model framework for CBR system modeling that separates domain expertise from CBR technology implementation.

Findings

Facilitates easier CBR system development

Enhances understanding of CBR modeling process

Supports broader application of CBR outside computer science

Abstract

Artificial intelligence (AI) has been used in various areas to support system optimization and find solutions where the complexity makes it challenging to use algorithmic and heuristics. Case-based Reasoning (CBR) is an AI technique intensively exploited in domains like management, medicine, design, construction, retail and smart grid. CBR is a technique for problem-solving and captures new knowledge by using past experiences. One of the main CBR deployment challenges is the target system modeling process. This paper presents a straightforward methodological approach to model CBR-based applications using the concepts of abstract and concrete models. Splitting the modeling process with two models facilitates the allocation of expertise between the application domain and the CBR technology. The methodological approach intends to facilitate the CBR modeling process and to foster CBR use in various areas outside computer science.