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BNAIC/BENELEARN 2022 : BNAIC/BENELEARN 2022 : The 34rd Benelux Conference on Artificial Intelligence and the 31th Belgian Dutch Conference on Machine Learning


When Nov 7, 2022 - Nov 9, 2022
Where Mechelen, Belgium
Abstract Registration Due Sep 26, 2022
Submission Deadline Oct 2, 2022
Notification Due Nov 3, 2022
Final Version Due Nov 17, 2022
Categories    artificial intelligence   machine learning   reinforcement learning   knowledge representation

Call For Papers

Call For Papers BNAIC/BeNeLearn 2022

BNAIC/BeNeLearn is the reference AI & ML conference for Belgium, Netherlands & Luxembourg. The combined conference will take place from November 7th till November 9th in Mechelen, Belgium and is organized by the University of Antwerp, under the auspices of the Benelux Association for Artificial Intelligence (BNVKI).

More information about the conference may be found at:


Researchers are invited to submit unpublished original research on all aspects of Artificial Intelligence and Machine Learning. Additionally, high-quality research results already published in international AI/ML conference proceedings 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 welcome. 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 to 16 pages, including references and appendices) or short ((= 11 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 published or accepted on or after June 1, 2021 to any AI/ML conference or journal. Authors are invited to submit the author version of their officially published paper together with an abstract of at most 2 pages (excluding references). Authors are encouraged to include further results obtained after the publication in their abstract and presentation. Submissions will be judged based on their originality and relevance to the conference. Authors may submit at most one type B paper of which they are the corresponding author.

Type C: Demonstration abstracts
Proposals for demos 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 be mentioned in the submission. 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
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, 2021. Submissions will be judged based on their originality and relevance to the conference.
Reviews will be done single-blind. All submissions should include 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 demonstration (type C), and best thesis (type D).

Accepted contributions in all four categories will be included in the online (non-archival) 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 2022 and present the paper in person at the conference. For each paper, a separate author registration is required.

Similar to previous years we plan to organize a post-proceedings in the Springer CCIS series. A selection of type A long papers will be invited to submit to the post-proceedings (

All deadlines are at 23:59, Anywhere on Earth time zone; please note that, in contrast with previous editions, the deadlines will not be extended.

Submission registration deadline: August 26, 2022
For all submission types, it is required to submit a title and an abstract of a few lines by Aug 26, 2022, for purposes of paper bidding and assignment to the reviewers.
Full submission deadline: September 2, 2022
Author notification: October 3, 2022
Camera ready submission deadline: October 17, 2022
Conference: November 7-9, 2022

For any questions please contact us at bnaicbenelearn2022(at)

We invite contributions on any topic in the broad area of Artificial Intelligence and Machine Learning. A non-exhaustive list of topics includes:
– Automated Machine Learning and Meta-learning
– Bayesian Learning
– Case-based Learning
– Causal Learning
– Clustering
– Computational Creativity
– Computational Learning Theory
– Computational Models of Human Learning
– Data Mining & Knowledge Discovery
– Data Visualisation
– Deep Learning
– Dimensionality Reduction
– Ensemble Methods
– Evaluation Frameworks
– Evolutionary Computation
– Graph Mining & Social Network Analysis
– Inductive Logic Programming
– Interactive AI / Human-in-the-loop Methods and Systems
– Kernel Methods
– 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 Data Science
– ML for Scientific Discovery
– ML in Non-stationary Environments
– 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
– Time series modeling & prediction
– Transfer and Adversarial Learning

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