Searching for Models with Hybrid AI Techniques

Description

The Model-Driven Engineering (MDE) [3] paradigm advocates for the use of models as an abstraction layer to represent complex systems. Model transformations are a central technique within MDE [10]. They either modify existing models or create new ones from scratch. Generally, these models should represent an optimal state of the system that has to be found within a large space of possible solutions. Model-driven optimization [1, 2, 4–6, 9] is a research area within MDE that proposes to automatically find optimal solutions which are constructed by a set of transformation rules given certain objectives. In order to search into large solution spaces, model-driven optimization approaches combine the expressiveness of models and domain-specific modeling languages, with the computational effectiveness of Artificial Intelligence (AI) methods to find the best model for a particular scenario. In this talk, we will present the framework Marrying Optimization and Model Transformations (MOMoT) which formulates the quest of finding the best models as an optimization problem [2, 8]. By this, MOMoT provides a general bridge between MDE and AI in which users may apply different AI techniques for the model search without requiring problem-specific encodings. MOMoT is built atop of the Eclipse Modeling Framework (EMF) using Henshin as a model transformation tool and MOEA for providing different evolutionary algorithms for performing the search process. In a recent work, we extended MOMoT with reinforcement learning approaches for performing the search process [7].

Period	18 Oct 2021
Event title	3rd International Workshop on Conceptual Modeling Meets Artificial Intelligence (CMAI 2021), co-Located with the 40th International Conference on Conceptual Modeling (ER 2021), 18-21 October 2021, St. Johns, Canada, virtual
Event type	Conference
Location	AustriaShow on map