Towards a Standard-based Domain-specific Platform to Solve Machine Learning-based Problems - Cueva-Lovelle, Juan Manuel; García-Díaz, Vicente; Pelayo García-Bustelo, B. Cristina; Pascual-Espada, Jordán (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 2020)
Machine learning is one of the most important subfields of computer science and can be used to solve a variety of interesting artificial intelligence problems. There are different languages, framework and tools to define the data needed to solve machine learning-based problems. However, there is a great number of very diverse alternatives which makes it difficult the intercommunication, portability and re-usability of the definitions, designs or algorithms that any developer may create. In this paper, we take the first step towards a language and a development environment independent of the underlying technologies, allowing developers to design solutions to solve machine learning-based problems in a simple and fast way, automatically generating code for other technologies. That can be considered a transparent bridge among current technologies. We rely on Model-Driven Engineering approach, focusing on the creation of models to abstract the definition of artifacts from the underlying technologies.
Keywords: MDE; DSL; artificial intelligence; machine learning; Xtext; IJIMAI