| Subject: | [WI] CfP: 2nd Workshop on Artificial Intelligence and Model-driven Engineering (MDE Intelligence 2020) @ Models 2020 |
|---|---|
| Date: | Wed, 10 Jun 2020 09:29:04 +0200 |
| From: | Manuel Wimmer <manuel.wimmer@jku.at> |
| Reply-To: | Manuel Wimmer <manuel.wimmer@jku.at> |
| To: | wi@lists.kit.edu |
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2nd Workshop on Artificial Intelligence and Model-driven
Engineering (MDE Intelligence 2020)
18-20 October 2020
Montreal, Canada
https://mde-intelligence.github.io/
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Theme & Goals
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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 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 an 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.
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TOPICS OF INTEREST
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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 explainable AI.
Topics of interest for the workshop include, but are not limited
to:
AI for MDE
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* 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, chatbots 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;
* Use of AI techniques in data, process and model mining and
categorisation;
* Natural language processing applied to modelling;
* Perception and modeling.
MDE for AI
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* 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;
* Model-based testing of AI components.
General
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* Tools for combining AI and MDE;
* Case studies in MDE Intelligence;
* Challenge problems to be addressed by combining AI and MDE
techniques.
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SUBMISSIONS
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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) Work-in-progress papers: 10 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=mdeintelligence2020.
Each submission 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 ACM Satellite Event Proceedings.
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IMPORTANT DATES
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Abstract submission: July 15, 2020
Paper submission: July 22, 2020
Notification: August 21, 2020
Camera-ready: August 28, 2020
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PROGRAM COMMITTEE
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Gregor Engels (University of Paderborn)
Moharram Challenger (University of Antwerp)
Shekoufeh Kolahdouz Rahimi (University of Isfahan)
Aurora Ramírez (University of Córdoba)
Gunter Mussbacher (McGill University)
Shaukat Ali (Simula Research Laboratory)
Nelly Bencomo (Aston University)
Francisco Chicano (University of Málaga)
Daniel Strüber (Chalmers University & University of
Gothenburg)
Adrian Rutle (Western Norway University of Applied Sciences)
Daniel Varro (McGill University & Budapest University of
Technology and Economics)
Talbi El-Ghazali (University of Lille)
Gabriele Taentzer (Philipps-Universität Marburg)
Bernhard Rumpe (RWTH Aachen University)
Betty Cheng (Michigan State University)
Shuai Li (CEA LIST)
Sandeep Neema (Vanderbilt University)
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Contact
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For additional information, clarification, or answers to
questions, please contact the Organizing Committee (Loli
Burgueño, Steffen Zschaler and Manuel Wimmer) by email at mdeintelligence2020@easychair.org
-- Manuel Wimmer Full Professor Head of Department Business Informatics – Software Engineering https://www.se.jku.at Head of the Christian Doppler Laboratory for Model-Integrated Smart Production (CDL-MINT) https://cdl-mint.se.jku.at Program Director Business Informatics (Master) https://www.jku.at/en/business-school Johannes Kepler University Linz Altenberger Straße 69 Science Park 3, Room S3 0077 4040 Linz, Austria