Model-Driven Development Towards Distributed Intelligent Systems

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 such as 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 modeling of DISs conforming to this metamodel. Subsequently, the DSL enables formal validation of the modeled systems. Lastly, it allows the generation and deployment of all DISs into production. Therefore, the work undertaken in this communication provides a methodological, formal, and standardized approach to defining and developing DISs from a high level of abstraction. This work allows users to address DISs by facilitating agility, minimizing manual tasks, and reducing the number of defects introduced in their development. To illustrate the applicability of the proposed DSL, a real case study of an agricultural digital twin is presented.