BNAIC/BENELEARN 2020 Call for papers

This year, the 32th Benelux Conference on Artificial Intelligence and the 29th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2020) are organized as a joint conference by Leiden University, under the auspices of the Benelux Association for Artificial Intelligence (BNVKI) and the Dutch Research School for Information and Knowledge Systems (SIKS).

BNAIC/BENELEARN 2020 will be held online, as a two-day event: from Thursday 19 to Friday 20 November, 2020. BNAIC/ BENELEARN 2020 will include invited speakers, research presentations, posters and demonstrations. The two-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Researchers are invited to submit unpublished original research on all aspects of Artificial Intelligence and Machine Learning.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 in AI/ML journals are also welcome. Four types of submissions are invited:

Type A: Regular papers

Papers presenting original work that advances Artificial Intelligence and Machine Learning. In addition to papers on original work, 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 or short. Long papers should be 10-15 pages, short papers 6-10 pages. Contributions will be reviewed on overall quality and relevance.

Type B: Compressed contributions

Abstracts of already published work. Papers that have been accepted after June 1, 2019 for AI/ML-related refereed conferences or journals can be resubmitted and will be accepted as compressed contributions. Authors are invited to submit the officially published version (without page restriction) together with a 2-page abstract. Authors may submit at most one type B paper of which they are the corresponding author.

Type C: Demonstrations

Demonstration abstracts. Proposals for demonstration should be submitted as a 2-page abstract together with a short video illustrating the working of the system (not exceeding 15 minutes). 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. Any system requirements should also be mentioned.

Type D: Thesis abstracts

Abstracts of graduation reports. Bachelor and Master students are invited to submit a 2-page abstract of their completed AI/ML-related thesis. Supervisors should be listed. The thesis should be accepted after June 1, 2019. Type D papers will be judged on relevance for the conference and originality.



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



Just like last years, there will be prizes for the best type A paper (regular paper), the best demonstration and the best type D paper (thesis abstract). This year there will also be two best video awards.



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 2020 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 8, 2020 (EXTENDED)
  • Author notification: October 5, 2020
  • Camera ready submission deadline: October 26, 2020
  • All deadlines are at 23:59, AoE time zone.
  • Conference: November 19-20, 2020



A non-exhaustive list of topics includes:

  • Automated Machine Learning and meta-learning
  • Bayesian Learning
  • Case-based Learning
  • Causal Learning
  • Clustering
  • Computational Learning Theory
  • Computational Models of Human Learning
  • Data Mining
  • Deep Learning
  • Ensemble Methods
  • Evaluation Frameworks
  • Evolutionary Computation
  • Feature Selection and Dimensionality Reduction
  • Inductive Logic Programming
  • Kernel Methods
  • Knowledge Discovery in Databases
  • Learning and Ubiquitous Computing
  • Learning for Language and Speech
  • Learning from Big Data
  • Learning in Multi-Agent Systems
  • 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
  • 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
  • Visual Analytics and ML
This year we encourage authors to submit academic work on the intersection of AI and games, such as (but not limited to):
  • Agent AI in Games
  • Procedural Content Generation in Games
  • Affective Computing in Games
  • Player Modeling in Games”