| Credit card fraud detection using Bayesian and neural networks S Maes, K Tuyls, B Vanschoenwinkel, B Manderick Proceedings of the 1st international naiso congress on neuro fuzzy …, 2002 | 365 | 2002 |
| A unified game-theoretic approach to multiagent reinforcement learning M Lanctot, V Zambaldi, A Gruslys, A Lazaridou, K Tuyls, J Pérolat, D Silver, ... Advances in neural information processing systems, 4190-4203, 2017 | 256 | 2017 |
| Evolutionary dynamics of multi-agent learning: A survey D Bloembergen, K Tuyls, D Hennes, M Kaisers Journal of Artificial Intelligence Research 53, 659-697, 2015 | 182 | 2015 |
| Deep reinforcement learning with relational inductive biases V Zambaldi, D Raposo, A Santoro, V Bapst, Y Li, I Babuschkin, K Tuyls, ... International Conference on Learning Representations, 2018 | 181* | 2018 |
| Inference of concise DTDs from XML data GJ Bex, F Neven, T Schwentick, K Tuyls Proceedings of the 32nd international conference on Very large data bases …, 2006 | 157 | 2006 |
| Multiagent learning: Basics, challenges, and prospects K Tuyls, G Weiss Ai Magazine 33 (3), 41-41, 2012 | 151 | 2012 |
| Cube and conquer: Guiding CDCL SAT solvers by lookaheads MJH Heule, O Kullmann, S Wieringa, A Biere Haifa Verification Conference, 50-65, 2011 | 142 | 2011 |
| Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward. P Sunehag, G Lever, A Gruslys, WM Czarnecki, VF Zambaldi, ... AAMAS, 2085-2087, 2018 | 137 | 2018 |
| The mechanics of n-player differentiable games D Balduzzi, S Racaniere, J Martens, J Foerster, K Tuyls, T Graepel arXiv preprint arXiv:1802.05642, 2018 | 133 | 2018 |
| What evolutionary game theory tells us about multiagent learning K Tuyls, S Parsons Artificial Intelligence 171 (7), 406-416, 2007 | 129 | 2007 |
| A selection-mutation model for q-learning in multi-agent systems K Tuyls, K Verbeeck, T Lenaerts Proceedings of the second international joint conference on Autonomous …, 2003 | 126 | 2003 |
| An evolutionary dynamical analysis of multi-agent learning in iterated games K Tuyls, PJT Hoen, B Vanschoenwinkel Autonomous Agents and Multi-Agent Systems 12 (1), 115-153, 2006 | 122 | 2006 |
| Value-decomposition networks for cooperative multi-agent learning P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ... arXiv preprint arXiv:1706.05296, 2017 | 108 | 2017 |
| Multi-robot collision avoidance with localization uncertainty. D Hennes, D Claes, W Meeussen, K Tuyls AAMAS, 147-154, 2012 | 104 | 2012 |
| Efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone K McGuire, G De Croon, C De Wagter, K Tuyls, H Kappen IEEE Robotics and Automation Letters 2 (2), 1070-1076, 2017 | 103 | 2017 |
| Evolutionary game theory and multi-agent reinforcement learning K Tuyls11, A Nowé | 99 | 2005 |
| Theoretical advantages of lenient learners: An evolutionary game theoretic perspective L Panait, K Tuyls, S Luke Journal of Machine Learning Research 9 (Mar), 423-457, 2008 | 98 | 2008 |
| How to reach linguistic consensus: A proof of convergence for the naming game B De Vylder, K Tuyls Journal of theoretical biology 242 (4), 818-831, 2006 | 92 | 2006 |
| Lenient multi-agent deep reinforcement learning G Palmer, K Tuyls, D Bloembergen, R Savani arXiv preprint arXiv:1707.04402, 2017 | 91 | 2017 |
| Emergence of linguistic communication from referential games with symbolic and pixel input A Lazaridou, KM Hermann, K Tuyls, S Clark arXiv preprint arXiv:1804.03984, 2018 | 90 | 2018 |
Gerhard WeissProfessor of Computer Science and Artificial Intelligence, Maastricht UniversityVerified email at maastrichtuniversity.nl
