-------- Forwarded Message --------
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
3rd Workshop on Artificial Intelligence and Model-Driven
Engineering (MDE Intelligence 2020) @ MODELS
10-15 October 2021
Virtual (Fukuoka, Japan)
https://mde-intelligence.github.io/
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
-=-=-=-=-=-=-=-
THEME & GOALS
-=-=-=-=-=-=-=-
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 industry 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 provides a forum to discuss, study and explore the
opportunities and challenges raised by the integration of AI and
MDE.
We would like to address topics such as 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 in order to increase the benefits and reduce the costs
of adopting MDE. We believe that AI artifacts will empower the MDE
tools and boost hence the advantages, and then adoption, of MDE at
industry level.
At the same time, 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.
Last but not least, 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.
-=-=-=-=-=-=-=-=-=-
TOPICS OF INTEREST
-=-=-=-=-=-=-=-=-=-
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.
Topics of interest for the workshop include, but are not limited
to:
AI for MDE
=-=-=-=-=-=-
* Application of (meta-heuristic) search to modelling problems;
* Machine learning of models, meta-models, concrete syntax, model
transformations, etc.;
* AI planning applied to modelling, meta-modelling, and model
management;
* Modeling assistants such as bots, conversational agents and
virtual assistants/recommenders supporting diverse modeling tasks;
* Model inferencers and automatic model generators from datasets;
* Self-adapting code generators;
* AI-based user interface adaptation for modeling tools;
* AI with human-in-the-loop for modeling;
* Semantic reasoning platforms over domain-specific models;
* Semantic integration of design-time models with runtime data;
* General-knowledge or domain-specific ontologies;
* Probabilistic models;
* AI techniques for data, process and model mining and
categorisation;
* Natural language processing applied to modelling;
* Perception and modeling
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 AI;
* Using models for knowledge representation;
* Code-generation for AI libraries and platforms;
* Architectural languages for AI-enhanced systems;
* Model-based testing of AI components.
General
=-=-=-=-
* Tools for combining AI and MDE;
* Case studies in MDE Intelligence;
* Experience reports of combining AI and MDE;
* Challenge problems to be addressed by combining AI and MDE
techniques.
=-=-=-=-=-=-
SUBMISSIONS
=-=-=-=-=-=-
Papers will follow the same style and format of the main tracks of
the conference (please check them here). We ask for two type of
contributions:
1) Research papers: 8 pages,
2) Vision papers, experience papers or demos: 5 pages.
Submissions must be uploaded through EasyChair in the following
link
https://easychair.org/conferences/?conf=mdeintelligence2021
.
All submissions will follow a single-blind review process where
each paper will be reviewed by at least 3 members of the program
committee. They will value the relevance and interest for
discussions that will take place at the workshop. Accepted papers
will be published in the IEEE MODELS Companion Proceedings.
Papers submitted to MDE Intelligence 2021 must not be under review
or submitted for review elsewhere whilst under consideration for
MDE intelligence 2021. Contravention of this concurrent submission
policy (as stated explicity by the IEEE on
https://www.comsoc.org/publications/ieee-communications-society-policy-plagiarism-and-multiple-submissions)
will be deemed as a serious breach of scientific ethics, and
appropriate action will be taken in all such cases.
=-=-=-=-=-=-=-=-=
IMPORTANT DATES
=-=-=-=-=-=-=-=-=
Paper submission: July 15, 2021
Notification: August 21, 2021
Camera-ready: August 28, 2021
Workshop: October 10-15, 2021
=-=-=-=-=-=-=-=-=-=
PROGRAM COMMITTEE
=-=-=-=-=-=-=-=-=-=
Shaukat Ali (Simula Research Laboratory, Norway)
Ángela Barriga (Western Norway University of Applied Sciences,
Norway)
Dominik Bork (TU Wien, Austria)
Jessie Carbonnel (Université de Montréal, Canada)
Francisco Chicano (University of Málaga, Spain)
Ludovico Iovino (Gran Sasso Science Institute, Italy)
Lawrence Mandow (University of Málaga, Spain)
Shekoufeh Kolahdouz Rahimi (University of Isfahan, Irán)
Aurora Ramírez (University of Córdoba, Spain)
Bernhard Rumpe (RWTH Aachen University, Germany)
Adrian Rutle (Western Norway University of Applied Sciences,
Norway)
Daniel Strüber (Radboud University Nijmegen, Netherlands)
Matthew Stephan (Miami University, USA)
Gabriele Taentzer (Philipps-Universität Marburg, Germany)
-=-=-=-=-=-
Contact
-=-=-=-=-=-
For additional information, clarification, or answers to
questions, please contact the Organizing Committee (Loli
Burgueño, Marouane Kessentini, Steffen Zschaler and Manuel
Wimmer) by email at
mdeintelligence2021@easychair.org