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BNAIC/BENELEARN 2021 : The 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning


When Nov 10, 2021 - Nov 12, 2021
Where Belval, Esch-sur-Alzette, Luxembourg
Submission Deadline Sep 10, 2021
Notification Due Oct 8, 2021
Final Version Due Oct 15, 2021
Categories    artificial intelligence   machine learning   reinforcement learning   knowledge representation

Call For Papers

The 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2021) are organised as a joint conference by the University of Luxembourg, under the auspices of the Faculty of Science, Technology and Medicine (FSTM) and the Interdisciplinary Lab for Intelligent and Adaptive Systems (ILIAS), and the IT for Innovative Services (ITIS) research department from the Luxembourg Institute of Science and Technology.

BNAIC/BENELEARN 2021 will be held in a hybrid online/onsite format from Wednesday 10 to Friday 12 November, 2021. BNAIC/BENELEARN 2021 will include invited speakers, research presentations, posters and demonstrations. The three-day conference will provide ample opportunity for interaction between academics and businesses: academics are also encouraged to join the business sessions, and vice versa.

Researchers are invited to submit unpublished original research on all aspects of Artificial Intelligence and Machine Learning. Additionally, high-quality research results already published at international AI/ML conferences or journals are also welcome as extended abstracts. Four types of submissions are invited:

Type A: Regular papers
Papers presenting original work that advances Artificial Intelligence and Machine Learning. Position and review papers are also welcomed. These contributions should address a well-developed body of research, an important new area, or a promising new topic, and provide a big picture view. Type A papers can be long ()=12 pages, including references and appendices) or short ((12 pages, including references and appendices). Contributions will be reviewed on the basis of their overall quality and relevance.

Type B: Encore abstracts
Abstracts of already published work that has been accepted in 2021 to any AI/ML conference or journal. Authors are invited to submit the author version of their officially published paper together with a 2-page abstract (excluding references). Authors may submit at most one type B paper of which they are the corresponding author.

Type C: Posters and demonstrations
Posters and demonstration abstracts. Proposals should be submitted as a 2-page (excluding references) abstract. Demonstrations should also submit a short video illustrating the working of the system (not exceeding 15 minutes). Any system requirements should also be mentioned in the submission. Posters and demonstrations will be evaluated based on their originality and innovative character, the technology deployed, the purpose of the systems in interaction with users and/or other systems, and their economic and/or societal potential.

Type D: Thesis abstracts
Abstracts of graduation reports. Bachelor and Master students are invited to submit a 2-page abstract (excluding references) of their completed AI/ML-related thesis. Supervisors should be listed. The thesis should have been accepted after June 1, 2020. Submissions will be judged based on their originality and relevance for the conference.

All submissions should not be anonymous, i.e. they must include all author names and their affiliations.

Type A, B, and D papers can be accepted for either oral or poster presentation.

Just like past years, there will be prizes for the best paper (type A), best poster and demonstration (type C), and best thesis (type D).
The best paper will be automatically nominated to the yearly special issue of AI Communication on Best of AI Research in Europe.

Accepted contributions within all four categories will be included in the online conference proceedings. All contributions should be written in English, using the Springer CCIS/LNCS format (see and submitted electronically via EasyChair:

Submission implies willingness of at least one author to register for BNAIC/BENELEARN 2021 and present the paper. For each paper, a separate author registration is required.

Selected Type A long papers will be invited to submit to the postproceedings published in Springer’s CCIS series (

– Paper submission deadline: September 10, 2021
– Author notification: October 8, 2021
– Camera ready submission deadline: October 15, 2021
– All deadlines are at 23:59, AoE time zone:
– Conference: November 10-12, 2021

This year we encourage authors to submit academic work at the intersection of:

AI & Arts
The town where the conference will take place, Esch sur Alzette will be the Cultural Capital of Europe in 2022. For this event the University of Luxembourg will create an AI&Art Pavilion, which aims to reflect on AI and the future of art including AI generated painting, AI painting style transfer, AI & Music, etc. In this emerging and hot topic, we expect papers exploring the relations between AI and Art from various points of view from using AI as a technology for art production to art production questioning the place of AI in our societies.

AI & Law
The application of AI tools in and for the legal domain is a manifold and continuously enriching area with quickly increasing interest from and involvement of both the legal professionals and AI researchers. The theoretical foundations and applications in AI & Law don’t only aim at modeling legal reasoning, providing analysis of trends and making legal tasks easier and more efficient, but also at providing foundations for law-abiding artificial agents. The topics range from rule-based reasoning, case-based reasoning, and formal legal ontologies, through computational legal argumentation, theory construction and legal deontics, until ML for legal analytics and RegTech.

AI & Ethics
The significant impact of AI, machine learning and robotics on society and the development of humanity is unquestionable. Its nature, controllability, tools, dangers and potential constraints have been subject to hot debates notably when AI is used in applications with sensitive ethical consequences (e-health, surveillance, human resources, micro-finance, etc.) since this raises concern about its fairness, accountability, and transparency. Thus, especially with the recent debates about user privacy and the Covid Tracking apps, this topic will remain a hot topic throughout the year of 2021.

AI & Systems
Over the last decade, computing became consistently ubiquitous and pervasive in all aspects of our private and professional lives, ending up in a seamless integration of distributed computing power, software, data, sensors, and actuators interacting with each other and with humans ultimately making the concept of ambient intelligence a reality as Cyber-Physical Social Systems. AI is central in such systems both as the functional computation building blocks and as means to create natural and seamless interactions among humans and between humans and their smart physical environment. From applying AI to IoT systems, paradigms like cognitive computing emerged and have raised the interest of the AI community. In this specific track, contributions on the application of AI on systems ranging from classic IoT to advanced cognitive systems including human in the loop are expected.

Automated Machine Learning and meta-learning
Bayesian Learning
Case-based Learning
Causal Learning
Computational Creativity
Computational Learning Theory
Computational Models of Human Learning
Data Mining
Data Visualisation
Deep Learning
Ensemble Methods
Evaluation Frameworks
Evolutionary Computation
Feature Selection and Dimensionality Reduction
Inductive Logic Programming
Interactive AI Methods and Applications
Kernel Methods
Knowledge Discovery in Databases
Learning and Ubiquitous Computing
Learning in Multi-Agent Systems
Learning from Big Data
Learning from User Interactions
Learning for Language and Speech
Media Mining and Text Analytics
ML and Information Theory
ML Applications in Industry
ML for Scientific Discovery
ML in Non-stationary Environments
ML with Expert-in-the-loop
Natural Language Processing / Natural Language Understanding
Neural Networks
Online Learning
Pattern Mining
Predictive Modeling
Ranking / Preference Learning / Information Retrieval
Reinforcement Learning
Representation Learning
Robot Learning
Social Networks
Statistical Learning
Structured Output Learning
Transfer and Adversarial Learning

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