BNAIC/BeNeLearn is the reference AI&ML conference for Belgium, Netherlands & Luxembourg. The combined 36th BNAIC and 33rd BeNeLearn conference will take place November 18-20 in Utrecht, the Netherlands. It is organized by Utrecht University, under the auspices of the Benelux Association for Artificial Intelligence (BNVKI) and in cooperation with the Netherlands Research School for Information and Knowledge Systems (SIKS).

SUBMISSION INFORMATION

Researchers are invited to submit their unpublished original research on all aspects of Artificial Intelligence and Machine Learning. In addition, this year we especially like to extend the invitation to submit original research on interdisciplinary topics. High-quality research results already published in international AI-related conference proceedings or journals are also welcome for presentation at the conference, and will be published as extended abstracts.

Four types of submissions are invited:

Type A: Regular papers Papers presenting original work that advances AI/ML. 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 will consist of a maximum of 14 pages (including text, figures, and tables; excluding references). Note that we also welcome (much) shorter papers. Contributions will be reviewed on the basis of their overall quality and relevance.

Type B: Encore abstracts Abstracts of work published (or accepted) in an international conference or journal relating to AI/ML and closely related fields. These should have been accepted on or after 1st September 2023. Authors are invited to submit the authors’ version of their officially published paper together with an abstract of at most 2 pages (excluding references). The abstract should have the same title as the original publication, and a reference to the published version. Authors are encouraged to include further results obtained after the publication in their abstract and presentation. Submissions will be judged based on their 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) abstracts. 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: Student Oral and Poster abstracts Bachelor and Master students are invited to submit a 2-page abstract (excluding references) of their AI/ML-related research based on their thesis. Note that this includes abstracts based on a bachelor or master thesis.  Supervisors and collaborators should be listed. Submissions will be judged based on their originality and relevance to the conference.  (Please see also “Bachelor / Master thesis fellowship”)

Type E: Late Breaking Poster abstracts Original and ongoing AI/ML-related work can be submitted as a Late Breaking abstract of 2-pages (excluding references). These late-breaking abstracts will not be selected for oral presentations.

Bachelor / Master thesis fellowship In addition to your Type D abstract, submit your Bachelor / Master thesis and grade list of your master/bachelor, in order to strongly reduce the registration fees; sponsored by the BNVKI. The thesis should have been accepted on or after 1st September 2024.

PRESENTATION AND PRIZES

Type A, B and D papers can be accepted for either oral or poster presentation. Type E, Late Breaking abstracts qualify for poster presentation only. There will be prizes for the best paper (type A), best demonstration (type C), and best bachelor/master thesis (in order to apply, submit a type D abstract accompanied by a thesis fellowship application).

REVIEWING POLICY

Reviews will be single-blind. All submissions should include author names and their affiliations.

IMPORTANT DATES

All deadlines are at 23:59, Anywhere on Earth time zone.

Submission

• Submission open (June)
• Abstract paper submission deadline,Type A: 23 August 2024 
• Paper submission deadline, all types except Type E: 30 August 2024 
• Bachelor / Master thesis fellowship, application deadline: 30 August 2024
• Late Breaking poster submission (Type E): 8 October 2024

Notification

• Author notification: 4 October 2024
• Bachelor / Master thesis fellowship notification: 4 October 2024
• Author notification for Late Breaking poster submission: 18 October 2024

Camera Ready

• Camera ready submission deadline: 25 October 2024

Conference 

• Conference: 18-20 November 2024

PRE- & POST-PROCEEDINGS

Pre-proceedings: Accepted contributions in all four categories will be included in the (non-archival) conference pre-proceedings. 

All contributions should be written in English, using the Springer CCIS/LNCS format (see https://easychair.org/my/conference?conf=bnaicbenelearn2024). Contributions must be submitted electronically via EasyChair.

Submission implies willingness of at least one author to register for BNAIC/BeNeLearn 2024 and present in person at the conference. For each paper, at least one author must register for the conference by the early registration deadline.

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

TOPICS OF INTEREST

We invite submissions in all areas of Artificial Intelligence and Machine Learning. In addition to fundamental work, this year we also encourage cross-sector and interdisciplinary work on AI, and the application of AI and ML-based techniques to topics such as social sciences and humanities, law and economics, neurosciences, bioinformatics and healthcare. 

A non-exhaustive list of topics includes:

AI methodology keywords

• 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
• Knowledge Representation and Reasoning
• Learning and Ubiquitous Computing
• Learning in Multi-Agent Systems
• Learning from Big Data
• Learning from User Interactions
• Logics and normative systems
• Media Mining and Text Analytics
• Natural Language Processing / Natural Language Understanding
• Online Learning
• Pattern Mining
• Ranking / Preference Learning / Information Retrieval
• Reinforcement Learning
• Representation Learning
• Robot Learning
• Social Networks
• Speech Recognition
• Structured Output Learning
• Time series modeling & prediction
• Transfer and Adversarial Learning

Impact area of research

• AI and Law
• AI and Ethics
• Bioinformatics, genomics and biomedical 
• AI and Economics (game theory)
• AI and Educational science
• Fundamental research in AI
• Human-centered AI
• Medical imaging
• AI and Neuroscience
• AI and Physics (complex systems)
• Scientific Machine Learning
• AI Applications in Industry
• Ai for Scientific Discovery
• AI and Social sciences
• Robotics 
• Ai and Gaming
• AI and Entertaining 
• AI and Agriculture 
• AI and Finance 
• AI and Transport 
• AI and Automotive
• AI and Social Media
• Data Security
• Healthcare
• E-Commerce
• AI and Art 
• AI and Astronomy