https://mde-intelligence.github.io
Model-driven engineering (MDE) and artificial intelligence (AI) are two separate fields in computer science, which can clearly benefit from cross-pollination and collaboration. There are at least two ways in which such integration—which we call MDE Intelligence—can manifest:
– Artificial Intelligence for MDE. MDE can benefit from integrating AI concepts and ideas to increase its power: flexibility, user experience, quality, etc. For example, using model transformations through search-based approaches, or by increasing the ability to abstract from partially formed, manual sketches into fully-shaped and formally specified meta-models and editors.
– MDE for Artificial Intelligence. AI is software, and as such, it can benefit from integrating concepts and ideas from MDE that have been proven to improve software development. For example, using domain-specific languages allows domain experts to directly express and manipulate their problems while providing an auditable conversion pipeline. Together this can improve trust in and safety of AI technologies. Similarly, MDE technologies can contribute to the goal of fair and explainable AI.
This workshop provides a forum to discuss, study and explore the opportunities and challenges raised by the integration of AI and MDE.
COORDINATES
22–24 September, 2024
Linz, Austria
Co-located with this year’s MODELS Conference.
TOPICS OF INTEREST
-AI for MDE
-Application of large language models (LLMs), Generative AI and machine learning to modelling problems;
-Machine learning and Generative AI for (meta-heuristic) search (meta)models, concrete syntax, model transformations, etc.;
-AI planning applied to (meta-)modelling, and model management;
-AI-supported modelling (e.g., bots, recommenders, UI adaptation, etc.);
-Model inferencers and automatic, dataset-based model generators;
-Self-adapting code generators;
-Semantic reasoning, knowledge graphs or domain-specific ontologies;
-AI-supported model-based digital twins;
-Probabilistic, descriptive or predictive models; AI techniques for data, process and model mining and categorisation;
-Natural language processing applied to modeling, including Large Language Models (LLM) and Generative AI;
-Data quality and privacy issues in AI for MDE;
-Reinforcement learning to optimize modelling tasks.
-MDE for AI
-Domain-specific modelling approaches for AI planning, machine learning, agent-based modelling, etc.;
-Model-driven processes for AI system development;
-MDE techniques for explainable and fair AI;
-Using models for knowledge representation;
-Code-generation for AI libraries and platforms;
-Architectural languages for AI-enhanced systems;
-MDE for federated learning;
-Model-based testing/analysis of AI components.
-General
-AI in teaching MDE;
-AI for MDE UX;
-Tools, frameworks, modeling standards;
-Experience reports, case studies, and empirical studies;
-Challenges.
IMPORTANT DATES
-July 5, 2024 – Paper submission deadline
-August 9, 2024 – Author notification
-August 14, 2024 – Camera-ready deadline
-September 22–24 – Workshop. Exact date TBA.
ORGANIZING COMMITTEE
–Lola Burgueño, Univ. of Malaga, Spain
–Dominik Bork, TU Wien, Austria
–Jordi Cabot, LIST, Luxembourg
–Sébastien Gérard, CEA LIST, France
–Aurora Ramírez, Univ. of Córdoba, Spain
FURTHER DETAILS