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.
Highlights
Artificial Intelligence (AI) and Machine Learning (ML) techniques are being extensively used in an ever-increasing number of areas and systems
The evaluation function is an optional facility that may be included in the Case-based Reasoning (CBR) operation process
The Evaluation Function (EV) is mapped from the objectives, attributes, and tolerances of the concrete model
Summary
How to cite this paper: Oliveira, E.M., Reale, R.F. and Martins, J.S.B. (2020) A Methodological Approach to Model CBRBased Systems. How to cite this paper: Oliveira, E.M., Reale, R.F. and Martins, J.S.B. (2020) A Methodological Approach to Model CBRBased Systems. Journal of Computer and Communications, 8, 1-16. Received: July 22, 2020 Accepted: September 1, 2020 Published: September 4, 2020
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