Model-Driven Development Towards Distributed Intelligent Systems

Abstract

A Distributed Intelligent System (DIS) encompasses a set of intelligent subsystems and components that collaborate to perform tasks and solve problems. Given the advancements of paradigms such as the Internet of Things, along with the advancements of technologies like Machine Learning and Digital Twins, DISs are on the rise. These systems are increasingly integrating components that perform intelligent functions, and these intelligent functions are increasingly heterogeneous and varied. Moreover, there is no standardized framework to help researchers and practitioners adequately address DISs. As a result, the complexity, interoperability issues, and development time and costs of these systems are growing. However, Model-Driven Development (MDD) can help to address these challenges by providing a Domain Specific Language (DSL) for developing DISs. In this work, a DSL for the design, validation, generation, and deployment of DISs is proposed. Firstly, the proposed DSL captures in a metamodel the key and high-level abstract concepts of the distinct DISs documented in the literature. Then, it allows to model DISs conforming to this metamodel. Subsequently, the DSL enables to formally validate the modeled systems. Lastly, it allows the generation and deployment of the entire DISs into production. Therefore, the work undertaken in this communication provides a methodological, formal and standardized approach to define and develop DISs from a high level of abstraction. Thus, allowing users to address DISs by facilitating agility, minimizing manual tasks, and reducing the number of defects introduced in their development. Furthermore, to illustrate the applicability of the proposed DSL, a real case study of an agricultural digital twin is illustrated.