The MDE Intelligence workshop is co-located with the IEEE/ACM 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS).
Artificial Intelligence (AI) has become part of everyone’s life. It is used by companies to exploit the information they collect to improve the products and/or services they offer and, wanted or unwanted, it is present in almost every device around us. Lately, AI is also starting to impact all aspects of the system and software development lifecycle, from their upfront specification to their design, testing, deployment and maintenance, with the main goal of helping engineers produce systems and software faster and with better quality while being able to handle ever more complex systems. The hope is that AI will help dealing with the increasing complexity of systems and software.
There is no doubt that MDE has been a means to tame until now part of this complexity. However, its adoption by industrial still relies on their capacity to manage the underlying methodological changes including among other things the adoption of new tools. To go one step further, we believe there is a clear need for AI-empowered MDE, which will push the limits of “classic” MDE and provide the right techniques to develop the next generation of highly complex model-based system and software systems engineers will have to design tomorrow.
This workshop will the opportunity to discuss how to choose, evaluate and adapt AI techniques to Model-Driven Engineering as a way to improve current system and software modeling and generation processes while, at the same time, increasing the benefits and reducing the costs of adopting MDE. Specially, we do believe that AI artifacts will empower the MDE tools and boost hence the advantages, and then adoption, of MDE at industry level. While AI is software (and complex software, in fact), we also believe that such AI-powered MDE approach will also benefit the design of AI artifacts themselves and specially to face the challenge of designing “trustable” AI software. Therefore, the workshop also aims to discuss this important issue of considering AI software itself as a targeted use case.
Although AI is the most popular branch of computer science to create and simulate intelligence, we also believe that any kind of technique that provides human cognitive capabilities and helps creating “intelligent” software are also in the scope of this workshop. An example would be the knowledge representation techniques and ontologies that can be useful on its own or support other kinds of AI techniques.
All dates are AoE.
Abstract submission: June 28, 2019 Paper submission: July 5, 2019 Notification: July 26, 2019
- Camera-ready: August 2, 2019
- Workshop date: September 15-17, 2019
- Sebastian Pilarski, Martin Staniszewski, Frederic Villeneuve and Daniel Varro “On Artificial Intelligence for Simulation and Design Space Exploration in Gas Turbine Design”
- Angela Barriga, Adrian Rutle and Rogardt Heldal “Personalized and automatic model repairing using reinforcement learning”
- Christopher Gerking and Ingo Budde “Heuristic Inference of Model Transformation Definitions from Type Mappings”
- Alexandru Burdusel and Steffen Zschaler “Towards Scalable Search-Based Model Engineering”
- Nicola Gatto, Evgeny Kusmenko and Bernhard Rumpe “Modeling and Training of Reinforcement Learning Architectures”
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