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Program

Sessions

Predictive models

Auditorium 9:30–10:45

Session chair: Wannes Meert

Gianluca Bontempi Between accurate prediction and poor decision making: the AI/ML gap (position paper)
Yuko Kato, David Tax and Marco Loog A view on model misspecification in uncertainty quantification
Wim Fyen, Wannes Meert, Bart Thijs and Koenraad Debackere Investigation of the predictive power of the human capital of academic researchers on their financial performance using social network analysis and decision trees
Nicolas Dewolf, Bernard De Baets and Willem Waegeman Valid prediction intervals for regression problems
Dimitrios Iliadis, Bernard De Baets and Willem Waegeman DeepMTP: a Python-based deep learning framework for multi-target prediction
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Time-series

Alcazar 9:30–10:45

Session chair: Toon Calders

Wolf De Wulf Transfer learning in Brain-Computer Interfaces: Language-Pretrained Transformers for Classifying Electroencephalography
Stijn Rotman and Boris Čule Forecasting Electric Vehicle Supply Equipment Availability
Victoria Catalán Pastor, Elena Congeduti, Aleksander Czechowski and Frans A. Oliehoek Overcoming Traffic Sensors Malfunctions with Deep Learning
Alejandro Morales-Hernández, Gonzalo Nápoles, Agnieszka Jastrzębska, Yamisleydi Salgueiro Sicilia and Koen Vanhoof Online learning of windmill time series using Long Short-term Cognitive Networks
Azqa Nadeem and Sicco Verwer SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting (Encore abstract)
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Industry talks

Dijlezaal 9:30–10:45

Session chair: Sophia Katrenko

Sam Landuydt Video recommendations on HLN
Robin Verachtert Froomle When and how to train recommendation models for better performance
Daan Hanssens An AI-powered platform to develop better products, faster
Stijn Meganck timeseer.ai Un(b)locking AI in industry
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Classification

Auditorium 11:30–12:30

Session chair: David Tax

Joseph Mietkiewicz and Anders Madsen Improvement of the fine tuning algorithm for naïve Bayes
Gadzhi Musaev, Kevin Mets, Tom De Schepper, Peter Hellinckx, Rokas Tamošiūnas and Vadim Uvarov On-Device Deep Learning Location Category Inference Model
Oliver Urs Lenz, Daniel Peralta and Chris Cornelis Optimised one-class classification performance
Bojian Yin, Federico Corradi and Sander Bohte Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
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RL & MDP

Alcazar 11:30–12:30

Session chair: Johan Kwisthout

Rolf Starre, Marco Loog and Frans Oliehoek Model-Based Reinforcement Learning with State Abstraction: A Survey
Elena Congeduti and Frans Oliehoek A Cross-Field Review of State Abstraction for Markov Decision Processes
Merlijn Krale, Thiago Simão and Nils Jansen The Value of Measuring in Q-learning for Markov Decision Processes
Florent Delgrange, Ann Nowé and Guillermo A. Pérez Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes
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Language & creativity

Dijlezaal 11:30–12:30

Session chair: Celine Vens

Ethelbert Uzodinma and Jennifer Spenader Improving Domain Robustness in Out-of-Domain Corpus
Cédric Goemaere, Thomas Demeester, Tim Verbelen, Bart Dhoedt and Cedric De Boom Efficient Keyword Generation using Pretrained Language Models
Lin de Huybrecht, Nick Harley and Geraint Wiggins Text Rendering for Automatic Story Generation
Luc Steels and Lara Verheyen Quantifying the contribution of different knowledge sources in text understanding
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Human+AI

Auditorium 14:00–15:00

Session chair: Elias Fernández

Bert Bredeweg and Marco Kragten Requirements and challenges for hybrid intelligence: A case-study in education
Emre Erdogan, Frank Dignum, Rineke Verbrugge and Pinar Yolum Computational Theory of Mind for Human-Agent Collaboration
Michiel van der Meer, Catholijn Jonker and Pradeep Kumar Murukannaiah HyEnA: Hybrid Intelligence for Argument Mining
Abdo Abouelrous, Yingqian Zhang and Laurens Bliek Digital Twin Applications in Urban Logistics: An Overview
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Scheduling

Alcazar 14:00–15:00

Session chair: Jefrey Lijffijt

Seppe Renty, Raphaël Avalos, Andries Rosseau and Ann Nowé Tackling Scheduling Problems With Graph Structured Reinforcement Learning.
Nikolaos Efthymiou and Neil Yorke-Smith Machine Learning for the Cyclic Hoist Scheduling Problem
Menno Oudshoorn, Timo Koppenberg and Neil Yorke-Smith Optimisation of Annual Planned Rail Maintenance (Article Abstract)
Issa Hanou, Mathijs de Weerdt and Jesse Mulderij Applying the Pebble Motion problem: studying the feasibility of the Train Unit Shunting Problem
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Logic

Dijlezaal 14:00–15:00

Session chair: Hendrik Blockeel

Nico Roos Specificity and context dependent preferences in argumentation systems
Pierre Carbonnelle, Simon Vandevelde, Joost Vennekens and Marc Denecker IDP-Z3: a reasoning engine for FO(.)
Johan Kwisthout Expanding Bayesian networks
Floris Geerts, Jasper Steegmans and Jan Van den Bussche On the expressive power of message-passing neural networks as global feature map transformers
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Posters & Demos

Poster Session 1

Mechelen Centraal 10:30–11:30

Posters of previous sessions:

Demos

Mechelen Centraal 10:30–12:30
Marcio Fuckner, Suzana Bašić, Pascal Wiggers, Yuri Westplat, Rick van Kersbergen and Iman Firouzifard BubbleMachine: An agent-based modelling tool to analyse the interplay between cognitive preferences, social network interactions and algorithmic curation
Matthias Müller-Brockhausen and Hélène Plisnier Transferring While Playing the RL Agent
Jeff van de Kamer, Maaike Hovenkamp, Erik Puik and Diederik M. Roijers Monitoring Diabetic Foot Ulceration Treatment with Smart Insoles and Neural Networks
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Poster Session 2

Mechelen Centraal 15:00–16:00

Posters of previous sessions:

Invited Talk

Auditorium 16:30–17:30

Session chair: Toon Calders

When responsible AI research needs to meet reality

Responsible AI topics are now firmly established in the research community, with explainable AI or AI fairness as flagships. On the other end of the spectrum, those topics are now seriously attracting attention in organizations and companies, especially through the lens of AI governance, a discipline that aims to analyze and address the challenges arising from a widespread use of AI in practice, as AI regulations are around the corner.

This leads companies to focus on the compliance aspects of AI projects. In practice, data scientists and organizations are in a fog, missing adequate guidance and solutions from research to achieve responsible AI.

In this talk, we will discuss how large companies, like AXA, currently see the responsible AI topic and why the current research output only provides partially actionable methodologies and solutions. We will discuss and illustrate with some concrete examples how the research community could better address the scientific challenges of this new applied responsible AI practice.

Marcin Detyniecki
AXA, France
Short bio

Marcin Detyniecki is Group Chief Data Scientist & Head of AI Research and Thought Leadership at insurance global leader AXA. He leverages his expertise to help AXA deliver value and overcome AI and ML-related business challenges and enable the group to achieve its transformation as a tech-led company. He leads the artificial intelligence R&D activity at group level. His team works on setting a framework enabling fair, safe and explainable ML to deliver value.

Marcin is also active in several think and do tanks, including a role of vice-president and board member of Impact AI, member of the Consultative Expert Group on Digital Ethics in Insurance for EIOPA and technical expert at Institut Montaigne. He has been involved in several academic roles including Research Scientist at both CNRS and Sorbonne University. He holds a Ph.D. in Computer Science from Université Pierre et Marie Curie.

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Sessions

NLP

Auditorium 9:00–10:30

Session chair: Paul Van Eecke

Jens Nevens, Jonas Doumen, Paul Van Eecke and Katrien Beuls Language Acquisition through Intention Reading and Pattern Finding
Paul Keuren, Marc Ponsen and Ayoub Bagheri WordGraph2Vec: Combining domain knowledge with embeddings
Jeska Buhmann, Maxime De Bruyn, Ehsan Lotfi and Walter Daelemans Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model
Marlon Saelens, Marie-Francine Moens, Ruben Cartuyvels and Liesbeth Allein Implicit Reasoning over Temporal Relations in Evidence-Based Fact-Checking
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Meta-learning

Alcazar 9:00–10:30

Session chair: Gianluca Bontempi

Ozgur Taylan Turan, David M.J. Tax and Marco Loog When MAML Learns Quickly, Does It Generalize Well?
Zhiyi Chen, Marco Loog and Jesse H. Krijthe Explaining Two Strange Learning Curves
Donghwi Kim and Tom Viering Different approaches to fitting and extrapolating the learning curve
Felix Mohr, Tom Viering, Marco Loog and Jan van Rijn LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks
Prajit Bhaskaran and Tom Viering To Tune or not to Tune: Hyperparameter Influence on the Learning Curve
Konstantinos Theodorakos, Mauricio Agudelo, Joachim Schreurs, Johan Suykens and Bart De Moor Ozone forecasting across Belgium with co-evolutionary Neural Architecture Search
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Health applications

Dijlezaal 9:00–10:30

Session chair: Isel Grau

Arnon Vandenberghe, Lyse Naomi Wamba Momo, Vincent Scheltjens and Bart De Moor Multimodal Deep Learning for Early Length of Stay Prediction using Patient Similarity Embeddings
Robbe Claeys, Yuju Ahn, Moobeom Hong, Inkyun Park and Jihwan Lim Towards a Systematic Investigation of Deep Learning Approaches for Bacterial Taxonomic Classification Using the 16S rRNA Gene
Nele Albers, Mark A. Neerincx, Kristell M. Penfornis and Willem-Paul Brinkman Using a Virtual Coach to Quit Smoking: 14 Themes for User Needs
Sander van Donkelaar, Lois Daamen, Paul Andel, Ralf Zoetekouw and Sharon Ong Superpixel-based Context Restoration for Self-supervised Pancreas Segmentation from CT scans
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Classification

Auditorium 11:30–12:30

Session chair: Sharon Ong

Yochem van Rosmalen, Florian van der Steen, Sebastiaan Jans and Daan van der Weijden Bursting the Burden Bubble? An Assessment of Sharma et al.’s Counterfactual-Based Fairness Metric
Steven Michiels, Lynn Houthuys, Cédric De Schryver, Frederik Vogeler and Frederik Desplentere Machine learning for automated quality control in injection moulding manufacturing
Rodi Laanen, Maedeh Nasri, Richard van Dijk, Mitra Baratchi, Alexander Koutamanis and Carolien Rieffe Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology
Afsana Khan, Marijn ten Thij and Anna Wilbik Communication-Efficient Vertical Federated Learning
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Reinforcement learning

Alcazar 11:30–12:30

Session chair: Diederik Roijers

Astrid Vanneste, Thomas Somers, Simon Vanneste, Kevin Mets, Tom De Schepper, Siegfried Mercelis and Peter Hellinckx Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning
Matthias Hutsebaut-Buysse, Kevin Mets, Tom De Schepper and Steven Latre Structured Exploration Through Instruction Enhancement for Object Navigation
Grigory Neustroev, Sytze Andringa, Remco Verzijlbergh and Mathijs de Weerdt Deep Reinforcement Learning for Active Wake Control
Santiago Amaya Modelling of human behaviour in traffic interactions using Inverse Reinforcement Learning
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Time-series

Dijlezaal 11:30–12:30

Session chair: Len Feremans

Rickard K.A. Karlsson, Martin Willbo and Fredrik D. Johansson Learning using Privileged Time-Series
Isel Grau, Michiel de Hoop, Ana Glaser, Gonzalo Nápoles and Remco Dijkman Semiconductor Demand Forecasting using Long Short-term Cognitive Networks
Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert and Hendrik Blockeel Unsupervised extraction and clustering of physical therapy exercise executions
Jean Dessain Improving the prediction of asset returns with machine learning by using a custom loss function
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Optimization

Auditorium 14:00–15:00

Session chair: Bart Bogaerts

Daimy Van Caudenberg and Bart Bogaerts Static Symmetry and Dominance Breaking for Pseudo-Boolean Optimization
Ya Song, Laurens Bliek and Yingqian Zhang Algorithm Selection for Traveling Salesman Problem with Simplified PointNet++
Sho Cremers, Valentin Robu, Daan Hofman, Titus Naber, Kawin Zheng and Sonam Norbu Efficient Methods for Approximating the Shapley Value for Asset Sharing in Energy Communities (Encore Abstract)
Dorian Vandenthoren Nonnegative Bilinear Matrix Factorization
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Multi-objective RL

Alcazar 14:00–15:00

Session chair: Pieter Libin

Conor F Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley and Patrick Mannion Expected scalarised returns dominance: a new solution concept for multi-objective decision making
Conor F Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel Ramos, Marcello Restelli, Peter Vamplew and Diederik M. Roijers Abstract: A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Willem Röpke, Diederik M. Roijers, Roxana Rădulescu and Ann Nowé Communication In Multi-Objective Games
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Computational creativity

Dijlezaal 14:00–15:00

Session chair: Joost Vennekens

Deniz Ezgi Kurt and Egberdien van der Peijl Is this art? A trans-disciplinary study of metadata in AI using the categorisation of ‘artists’ writing’ coined by the Art and Language Group (1968 – ).
Jeanine-Estelle Vallecalle Overview of psychometric tools to evaluate robotic creativity – A scoping review
Marnix Verduyn Comic Art Generation using GANs
Erinn Van der Sande, Nick Harley and Geraint Wiggins Fabula Generation with BCDI Characters and Narrative Tension
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FACt (FACulty focusing on the FACts of AI)

Auditorium 16:00–17:00
Khalid Al Khatib (University of Groningen)
Deliberation, Not Persuasion: How AI Can Support More Effective Discussions
Joost Vennekens (KU Leuven)Using Logic to Empower the User
Gianluca Bontempi (Université Libre de Bruxelles)AI decision-making and ethics: what data scientists should know
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Posters & Demos

Poster Session 1

Mechelen Centraal 10:30–11:30

Posters of previous sessions:

Demos

Mechelen Centraal 10:30–12:30
Willem Röpke, Samuele Pollaci, Bram Vandenbogaerde, Jiahong Li and Youri Coppens Multi-Objective Scheduling for Agricultural Interventions
Daphne Lenders and Toon Calders A New Benchmarking Dataset for Fair ML
Lucas N. Alegre, Florian Felten, El-Ghazali Talbi, Grégoire Danoy, Ann Nowé, Ana L. C. Bazzan and Bruno C. da Silva MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments
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Poster Session 2

Mechelen Centraal 15:00–16:00

Posters of previous sessions:

Invited Talk

Auditorium 17:00–18:00

Session chair: Celine Vens

Continual learning: Beyond solving datasets

Tinne Tuytelaars
KU Leuven, Belgium
Short bio

Tinne Tuytelaars is professor at KU Leuven, Belgium, working on computer vision and, in particular, topics related to image representations, vision and language, incremental learning, image generation and more. She has been program chair for ECCV14, general chair for CVPR16, and will again be program chair for CVPR21. She also served as associate-editor-in-chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence over the last four years. She was awarded an ERC Starting Grant in 2009 and received the Koenderink test-of-time award at ECCV16.

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Sessions

Explainable AI

Auditorium 9:00–10:30

Session chair: Jan Lemeire

Arne Gevaert and Yvan Saeys PDD-SHAP: Fast Approximations for Shapley Values using Functional Decomposition
Wai Wong, Walter Schaeken and Joost Vennekens On the importance of experimental psychology for explainable artificial intelligence
Raphaela Butz, Arjen Hommersom, Marco Barenkamp and Hans van Ditmarsch One counterfactual does not make an explanation
Roan Schellingerhout, Volodymyr Medentsiy and Maarten Marx Explainable Career Path Predictions using Neural Models
Suzana Bašić, Marcio Fuckner and Pascal Wiggers Explainable Misinformation Detection from Text: A Critical Look
Carla Wrede, Mark Winands and Anna Wilbik Linguistic Summaries as Explanation Mechanism for Classification Problems
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Image classification and segmentation

Alcazar 9:00–10:30

Session chair: Jef Lijffijt

Britt Schmitz and Marc Ponsen Change Detection of Land Use: A Deep Learning Case-Study
Jef Plochaet and Toon Goedemé Towards Automatic Proctoring of Online Exams Using Video Anomaly Detection
Arya Tri Prabawa, Merel Jung, Kostas Stoitsas, Werner Liebregts and Itır Önal Ertuğrul Predicting Probability of Investment Based on Investor’s Facial Expression in a Startup Funding Pitch
Amber Cassimon, Liam Hertoghs, Simon Vanneste, Phil Reiter, Kevin Mets, Tom De Schepper, Siegfried Mercelis and Peter Hellinckx Predicting Image Classifier Performance Using the Synthetic Petri Dish Method
Benjamin Rombaut, Joris Roels and Yvan Saeys BioSegment: Active Learning segmentation for 3D electron microscopy imaging
Mirela Popa, Marius Sommerfeld, Joël Karel, Stefan Schwartz and András Tóth 3D Plant Segmentation for High Throughput Phenotyping using 3D Morphological Algorithms
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Ontologies and knowledge graphs

Dijlezaal 9:00–10:30

Session chair: Jan Van den Bussche

Idries Nasim and Shuai Wang Examining the Evolution of Identity graphs Through Redirection
Tianyang Lu, Shuai Wang and Zhisheng Huang Towards Smart Urban Resilience: A Linked Data Approach
Romana Pernisch, Daniele Dell’Aglio, Mirko Serbak, Rafael Gonçalves and Abraham Bernstein Visualising the Effects of Ontology Changes and Studying their Understanding with ChImp
Loan Ho, Somjit Arch-Int, Erman Acar, Stefan Schlobach and Ngamnij Arch-Int An Argumentative Approach for Handling Inconsistency in Prioritized Datalog± Ontologies
Dimitrios Alivanistos, Max Berrendorf, Michael Cochez and Michael Galkin Query Embedding on Hyper-Relational Knowledge Graphs
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Classification

Auditorium 11:30–12:30

Session chair: Lynn Houthuys

Thi Hoang Anh Tran, Malina Lara Wiesner and Maurice van Keulen Influence of discretization granularity on learning classification models
Daniël Vos and Sicco Verwer Robust Optimal Classification Trees against Adversarial Examples
Tobias Glasmachers Recipe for Fast Large-scale SVM Training: Polishing, Parallelism, and more RAM!
Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczyński and Willem Waegeman Set-valued prediction in hierarchical classification
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Reinforcement learning

Alcazar 11:30–12:30

Session chair: Kevin Mets

Floris den Hengst, Vincent François-Lavet, Mark Hoogendoorn and Frank van Harmelen Reinforcement Learning with Option Machines
Louis Bagot, Kevin Mets, Tom De Schepper and Steven Latré A Case for Feature-Based Successor Features for Transfer in Reinforcement Learning
Ksenija Stepanovic, Jichen Wu, Rob Everhardt and Mathijs de Weerdt Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning
Julius Wagenbach and Matthia Sabatelli Factors of Influence of the Overestimation Bias of Q-Learning
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NLP

Dijlezaal 11:30–12:30

Session chair: Gerasimos (Jerry) Spanakis

Rastislav Hronsky and Emmanuel Keuleers Does the Choice of a Segmentation Algorithm Affect the Performance of Text Classifiers?
Sander Brinkhuijsen, Romana Pernisch, Eljo Haspels and Mark Van Staalduinen Context-Aware Feature Vectors in Dark Web Page Classification
Yawen Zhao and Jordy Van Landeghem Better Late than Never: Late Fusion Techniques for Document Classification
Daniel Daza, Michael Cochez and Paul Groth SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning
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Runner-up Award Nominees

Auditorium

Auditorium 14:00–14:45

Session chair: Toon Calders

Liesbet De Vos Human-Interpretable Grounded Language Processing
Salima Lamsiyah and Christoph Schommer A Comparative Study of Sentence Embeddings for Unsupervised Extractive Multi-Document Summarization

Alcazar

Alcazar 14:00–14:45

Session chair: Celine Vens

Shashank Subramanya and Gerasimos Spanakis MultiTM: A Multilingual Topic Modeling approach based on Clustering
Paul Wallbott, Sascha Grollmisch and Thomas Köllmer Examining speaker and keyword uniqueness: Partitioning keyword spotting datasets for federated learning with the largest differencing method

Dijlezaal

Dijlezaal 14:00–14:45

Session chair: Jef Lijffijt

William Dumez, Simon Vandevelde and Joost Vennekens Step-wise Explanations of Sudokus using IDP
Marco Loog and Tom Viering A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance
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Best paper, demo and thesis

Auditorium 16:00–17:00

Best demo announcement followed by presentations of the best paper and best thesis winners.

Fieke Middelraad This laptop has great coffee: Training a Dutch ABSA model from customer reviews
Kshitij Goyal, Wannes Meert, Hendrik Blockeel, Elia Van Wolputte, Koen Vanderstraeten, Wouter Pijpops and Kurt Jaspers Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming
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Posters & Demos

Poster Session 1

Mechelen Centraal 10:30–11:30

Posters of previous sessions:

Demos

Mechelen Centraal 10:30–12:30
Simon Vandevelde and Joost Vennekens FOLL-E: Teaching First Order Logic to Children
Tias Guns, Milan Pesa, Maxime Mulamba, Ignace Bleukx, Emilio Gamba and Senne Berden Sudoku Assistant – An AI-powered app to help solve pen-and-paper Sudokus
Ruben Janssens, Thomas Demeester and Tony Belpaeme Visual Conversation Starters for Human-Robot Interaction
Axel Abels, Elias Fernández Domingos, Tom Lenaerts, Vito Trianni and Ann Nowé Bias Mitigation in Decision-Making with Expert Advice
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Poster Session 2

Mechelen Centraal 14:45–16:00

Posters of previous sessions:

Invited Talk

Auditorium 17:00–18:00

Session chair: Jefrey Lijffijt

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning

In this talk I will introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few iconic board games that Artificial Intelligence (AI) had not yet mastered. This game has an enormous game tree, orders of magnitude bigger than that of Go and Texas hold’em poker. It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold’em poker. Decisions in Stratego are made over a large number of discrete actions with no obvious link between action and outcome. Episodes are long, with often hundreds of moves before a player wins, and situations in Stratego can not easily be broken down into manageable-sized sub-problems as in poker. For these reasons, Stratego has been a grand challenge for the field of AI for decades, and existing AI methods barely reach an amateur level of play. DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego via self-play and converges to an approximate Nash equilibrium, instead of ‘cycling’ around it, by directly modifying the underlying multi-agent learning dynamics. DeepNash beats existing state-of-the-art AI methods in Stratego and achieved a yearly and all-time top-3 rank on the Gravon games platform, competing with human expert players.

Karl Tuyls
DeepMind, France
University of Liverpool, UK
Short bio

Karl Tuyls (FBCS) is a team lead at DeepMind, an honorary professor of Computer Science at the University of Liverpool, UK, and a Guest Professor at the University of Leuven, Belgium. Previously, he held academic positions at the Vrije Universiteit Brussel, Hasselt University, Eindhoven University of Technology, and Maastricht University.

He is a fellow of the British Computer Society (BCS), is on the editorial board of the Journal of Autonomous Agents and Multi-Agent Systems, and is editor-in-chief of the Springer briefs series on Intelligent Systems. Prof. Tuyls is also an emeritus member of the board of directors of the International Foundation for Autonomous Agents and Multiagent Systems.

Prof. Tuyls has received several awards with his research, amongst which: the Information Technology prize 2000 in Belgium, best demo award at AAMAS’12, winner of various Robocup@Work competitions (’13, ’14), and he was a co-author of the runner-up best paper award at ICML’18. Furthermore, his research has received substantial attention from national and international press and media, most recently his work on Sports Analytics featured in Wired UK.

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