JournalBookBook ChaptersConference


Journal Articles

  1. Klima, R., Bloembergen, D., Savani, R., Tuyls, K., Hennes, D., & Izzo, D. (2016). Space Debris Removal: A Game Theoretic Analysis. Games, 7(3), 20. doi:10.3390/g7030020
  2. Ranjbar-Sahraei, B., Bou Ammar, H., Tuyls, K., & Weiss, G. (2016). On the prevalence of hierarchies in social networks. Social Network Analysis and Mining, 6(1). doi:10.1007/s13278-016-0363-8
  3. Rahmani, H., Ranjbar-Sahraei, B., Weiss, G., & Tuyls, K. (2016). Entity resolution in disjoint graphs: An application on genealogical data. INTELLIGENT DATA ANALYSIS, 20(2), 455-475. doi:10.3233/IDA-160814
  4. D, Bloembergen, K. Tuyls, D. Hennes, and M. Kaisers. Evolutionary Dynamics of Multi-Agent Learning: A Survey. Journal of Artificial Intelligence Research (JAIR), Volume 53, pages 659-697, 2015
  5. Daniel Hennes, Karl Tuyls and Ya’akov Gal. Metastrategies in large scale bargaining games. Accepted for publication in ACM Transactions on Intelligent Systems and Technology TIST, 2015.
  6. Rahmani Hossein, Ranjbar Bijan, Weiss Gerhard and Tuyls Karl. Entity Resolution in Disjoint Graphs: an Application on Genealogical Data. Accepted for publication in Intelligent Data Analysis, 2015.
  7. Decebal Mocanu, Haitham Bou Ammar, Dietwig Lowet, Kurt Driessens, Tuyls Karl, Antonio Liotta, and Gerhard Weiss. Factored Four Way Con- ditional Restricted Boltzmann Machines for Activity Recognition. Pattern Recognition Letters, 2015
  8. Bloembergen Daniel, Hennes Daniel, McBurney Peter, and Tuyls Karl (2015) Trading in markets with noisy information: an evolutionary analysis. Accep- ted for publication in Connection Science, 2015.
  9. Siqi Chen, Haitham Bou Ammar, Karl Tuyls, and Gerhard Weiss. Transfer for Automated Negotiation. Kunstliche Intelligenz, In press, 2014.
  10. Mihail Mihaylov, Karl Tuyls, and Ann Nowe. A decentralized approach for convention emergence in multi-agent systems. Autonomous Agents and Multi-Agent Systems, 2013.
  11. Nyree Lemmens and Karl Tuyls. Stigmergic Landmark Optimization. Advances in Complex Systems, 15(8), 2012.
  12. Mihail Mihaylov, Yann-Aël Le Borgne, Karl Tuyls, and Ann Nowé. Decentralised reinforcement learning for energy-efficient scheduling in wireless sensor networks. IJCNDS, 9(3/4):207–224, 2012.
  13. Mihail Mihaylov, Yann-Aël Le Borgne, Karl Tuyls, and Ann Nowé. Reinforcement Learning for Self-Organizing Wake-Up Scheduling in Wireless Sensor Networks. Communications in Computer and Information Science, 271:382–397, 2012.
  14. Karl Tuyls and Gerhard Weiss. Multiagent Learning: Basics, Challenges, and Prospects. AI Magazine, 33(3):41–52, 2012.
  15. Steven de Jong and Karl Tuyls. Human-inspired computational fairness. Autonomous Agents and Multi-Agent Systems, 22(1):103–126, 2011.
  16. David W. Aha, Mark S. Boddy, Vadim Bulitko, Artur S. d’Avila Garcez, Prashant Doshi, Stefan Edelkamp, Christopher W. Geib, Piotr J. Gmytrasiewicz, Robert P. Goldman, Pascal Hitzler, Charles L. Isbell, Darsana P. Josyula, Leslie Pack Kaelbling, Kristian Kersting, Maithilee Kunda, Luís C. Lamb, Bhaskara Marthi, Keith McGreggor, Vivi Nastase, Gregory Provan, Anita Raja, Ashwin Ram, Mark O. Riedl, Stuart J. Russell, Ashish Sabharwal, Jan-Georg Smaus, Gita Sukthankar, Karl Tuyls, Ron van der Meyden, Alon Y. Halevy, Lilyana Mihalkova, and Sriraam Natarajan. Reports of the AAAI 2010 Conference Workshops. AI Magazine, 31(4):95–108, 2010.
  17. Marc Ponsen, Karl Tuyls, Michael Kaisers, and Jan Ramon. An Evolutionary Game Theoretic Analysis of Poker Strategies. Entertainment Computing, 2009.
  18. Karl Tuyls, Franziska Kluegl, and Sandip Sen. Adaptive Agents and Multi-Agent Learning (preface). International Journal of Agent Technologies and Systems, 2009.
  19. Steven de Jong, Simon Uyttendaele, and Karl Tuyls. Learning to Reach Agreement in a Continuous Ultimatum Game. J. Artif. Intell. Res. (JAIR), 33:551–574, 2008.
  20. Steven de Jong, Karl Tuyls, and Katja Verbeeck. Fairness in multi-agent systems. Knowledge Eng. Review, 23(2):153–180, 2008.
  21. Liviu Panait, Karl Tuyls, and Sean Luke. Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective. Journal of Machine Learning Research, 9:423–457, 2008.
  22. Ben Torben-Nielsen, Karl Tuyls, and Eric O. Postma. EvOL-Neuron: Neuronal morphology generation. Neurocomputing, 71(4-6):963–972, 2008.
  23. Karl Tuyls and Simon Parsons. What evolutionary game theory tells us about multiagent learning. Artif. Intell., 171(7):406–416, 2007.
  24. Katja Verbeeck, Ann Nowé, Johan Parent, and Karl Tuyls. Exploring selfish reinforcement learning in repeated games with stochastic rewards. Autonomous Agents and Multi-Agent Systems, 14(3):239–269, 2007.
  25. Ronald L. Westra, Goele Hollanders, Geert Jan Bex, Marc Gyssens, and Karl Tuyls. The pattern memory of gene-protein networks. AI Communications, 20(4):297–311, 2007.
  26. Bart DeVylder and Karl Tuyls. How to Reach Linguistic Consensus: A Proof of Convergence for the Naming Game. Journal of Theoretical Biology, 242(4):818–831, 2006.
  27. Karl Tuyls, Pieter Jan’t Hoen, and Bram Vanschoenwinkel. An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games. Autonomous Agents and Multi-Agent Systems, 12(1):115–153, 2006.
  28. Tom Lenaerts, Bart Jansen, Karl Tuyls, and Bart DeVylder. The Evolutionary Language Game: An orthogonal approach. The Journal of Theoretical Biology, 235(4):566–582, 2005.
  29. Karl Tuyls and Ann Nowe. Evolutionary Game Theory and Multi-Agent Reinforcement Learning. The Knowledge Engineering Review, 20(01):63–90, 2005.
  30. Karl Tuyls, Ann Nowe, Tom Lenaerts, and Bernard Manderick. An Evolutionary Game Theoretic perspective on Learning in Multi-Agent Systems. Synthese, section Knowledge, Rationality and Action, 139, issue 2:297–330, 2004.



  1. Jonker, C., Marsella, S., Thangarajah, J., & Tuyls, K. (2016). Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. (2016 ed.). K. Tuyls (Ed.), ACM.

Book Chapters

  1. Chen, S., Zhou, S., Weiss, G., & Tuyls, K. (2016). Using transfer learning to model unknown opponents in automated negotiations. In Studies in Computational Intelligence Vol. 638 (pp. 175-192). doi:10.1007/978-3-319-30307-9_11

Conference Papers

  1. Collenette, J., Atkinson, K., Bloembergen, D., & Tuyls, K. (2016). The Effect of Mobility and Emotion on Interactions in Multi-Agent Systems. In PROCEEDINGS OF THE EIGHTH EUROPEAN STARTING AI RESEARCHER SYMPOSIUM (STAIRS 2016) Vol. 284 (pp. 39-50). doi:10.3233/978-1-61499-682-8-39
  2. Klima, R., Bloembergen, D., Savani, R., Tuyls, K., Hennes, D., & Izzo, D. (2016). Space Debris Removal: A Game Theoretic Analysis. In ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE Vol. 285 (pp. 1658-1659). doi:10.3233/978-1-61499-672-9-1658
  3. Li, X., Zhang, C., Hao, J., Tuyls, K., Chen, S., & Feng, Z. (2016). Socially-Aware Multiagent Learning: Towards Socially Optimal Outcomes. In ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE Vol. 285 (pp. 533-541). doi:10.3233/978-1-61499-672-9-533
  4. de Bruin, T., Kober, J., Tuyls, K., & Babushka, R. (2016). Improved Deep Reinforcement Learning for Robotics Through Distribution-based Experience Retention. In IROS 2016 – IEEE/RSJ International Conference on Intelligent Robots and Systems. Daejeon, Korea.
  5. Tuyls, K., Alers, S., Cucco, E., Claes, D., & Bloembergen, D. (2016, July 4). A Telepresence-Robot Approach for Efficient Coordination of Swarms. In The 15th International Conference on the Synthesis and Simulation of Living Systems (Alife). Cancun, Mexico.
  6. Kimberly Nancy McGuire, Guido de Croon, Bart Remes, Christophe De Wagter, Karl Tuyls, Bert Kappen. Local Histogram Matching for Efficient Optical Flow Computation Applied to Velocity Estimation on Pocket Drones. Accepted for publication at the 2016 IEEE International Conference on Robotics and Automation (ICRA)