Karl Tuyls Publications

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Classified by Research Category

• Multi-Agent Learning • Bioinformatics • Swarm Intelligence • Machine Learning - Datamining • Other •

Multi-Agent Learning

• Steven de Jong and Karl Tuyls. Human-Inspired Computational Fairness. Accepted in Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), 2010.
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• Michael Kaisers and Karl Tuyls. Frequency Adjusted Multi-Agent Q-learning. In Accepted at the international joint conference on Autonomous Agents and Multi-Agent Systems (AAMAS'10), 2010.
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• Daniel Hennes and Karl Tuyls. State-Coupled Replicator Dynamics. In Accepted at the international joint conference on Autonomous Agents and Multi-Agent Systems (AAMAS'09), 2009.
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• Steven de Jong and Karl Tuyls. Learning to cooperate in a continuous tragedy of the commons. In Accepted at the international joint conference on Autonomous Agents and Multi-Agent Systems (AAMAS'09), 2009.
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• Michael Kaisers, Karl Tuyls, Frank Thuijsman, and Simon Parsons. An Evolutionary Model of Multi-agent Learning with a Varying Exploration Rate. In Accepted at the international joint conference on Autonomous Agents and Multi-Agent Systems (AAMAS'09), 2009.
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• Marc Ponsen, Tom Croonenborghs, Karl Tuyls, Jan Ramon, and Kurt Driessens. Learning with whom to communicate using Relational Reinforcement Learning. In Accepted at the international joint conference on Autonomous Agents and Multi-Agent Systems (AAMAS'09), 2009.
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• Marc Ponsen, Karl Tuyls, Michael Kaisers, and Jan Ramon. An Evolutionary Game Theoretic Analysis of Poker Strategies. Accepted in Entertainment Computing, 2009.
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• Karl Tuyls, Franziska Kluegl, and Sandip Sen. Adaptive Agents and Multi-Agent Learning (preface). International Journal of Agent Technologies and Systems, 2009, in press.
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• Karl Tuyls and Ronald Westra. Replicator Dynamics in Discrete and Continuous Strategy Spaces . In Accepted for publication in Multi-Agent Systems: Simulation and Applications, 2009.
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• Daniel Hennes, Karl Tuyls, and Matthias Rauterberg. Formalizing Multi-State Learning Dynamics. In Accepted at IEEE IAT2008, Sydney, Australia, 2008.
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• Daniel Hennes, Kagan Tumer, and Karl Tuyls. A multiagent approach to hyperredundant manipulators. In Alag, ALAMAS 2008 workshop at AAMAS'08, Estoril, Portugal, May 12th, 2008.
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• Steven de Jong, Simon Uyttendaele, and Karl Tuyls. Learning to Reach Agreement in a Continuous Ultimatum Game. Journal of Artificial Intelligence Research (JAIR), 33:551–574, 2008.
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• Steven de Jong and Karl Tuyls. Learning to cooperate in public-goods interactions.. In Presented at the EUMAS'08 Workshop, December 18-19, Bath, UK, 2008.
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• Steven de Jong, Karl Tuyls, and Katja Verbeeck. Fairness in multi-agent systems. Knowledge Engineering Review, 23(02):153–180, 2008.
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• Steven de Jong, Karl Tuyls, and Katja Verbeeck. Artificial Agents Learning Human Fairness. In Accepted at the international joint conference on Autonomous Agents and Multi-Agent Systems (AAMAS'08), 2008.
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• Steven de Jong, Karl Tuyls, Katja Verbeeck, and Nico Roos. Priority awareness: towards a computational model of human fairness for multi-agent systems. In K. Tuyls, A. Nowe, Z. Guessoum, and D. Kudenko, editors, Adaptive Agents and Multi-Agent Systems III, Lecture Notes in Artificial Intelligence, pp. to appear, Springer Verlag, Berlin, 2008.
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• Steven de Jong, Rob van de Ven, and Karl Tuyls. The influence of physical appearance on a fair share . In Accepted at BNAIC2008, Twente, The Netherlands, 2008.
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• Michael Kaisers, Karl Tuyls, Frank Thuijsman, and Simon Parsons. Auction Analysis by Normal Form Game Approximation. In Accepted at IEEE IAT2008, Sydney, Australia, 2008.
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• Michael Kaisers, Karl Tuyls, Frank Thuijsman, and Simon Parsons. Discovering the Game in Auctions. In Accepted at BNAIC2008, Twente, The Netherlands, 2008.
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• Nyree Lemmens, Steven de Jong, Karl Tuyls, and Ann Nowé. Bee behaviour in multi-agent systems: A bee foraging algorithm. In K. Tuyls, A. Nowe, Z. Guessoum, and D. Kudenko, editors, Adaptive Agents and Multi-Agent Systems III, Lecture Notes in Artificial Intelligence, pp. to appear, Springer Verlag, Berlin, 2008.
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• Mihail Mihaylov, Ann Nowe, and Karl Tuyls. Collective Intelligent Wireless Sensor Networks . In Accepted at BNAIC2008, Twente, The Netherlands, 2008.
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• Liviu Panait, Karl Tuyls, and Sean Luke. Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective. Journal of Machine Learning Research (JMLR), 9:423–457, 2008.
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• Marc Ponsen, Karl Tuyls, Jan Ramon, and Kurt Driessens. The Evolutionary Dynamics of Poker. In Accepted for publication at the European Conference on Complex Systems, ECCS'08, 2008.
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• Marc Ponsen, Jan Ramon, Tom Croonenborghs, Kurt Driessens, and Karl Tuyls. Bayes-Relational Learning of Opponent Models from Incomplete Information in No-Limit Poker . In Accepted at AAAI 2008, 2008.
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• Marc Ponsen, Karl Tuyls, Steven de Jong, Jan Ramon, Tom Croonenborghs, and Kurt Driessens. The Dynamics of Human Behaviour in Poker. In Accepted at BNAIC2008, Twente, The Netherlands, 2008.
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• Matthias Rauterberg, Mark Neerincx, Karl Tuyls, and Jack van Loon. Entertainment Computing in the Orbit. In Accepted at ECS 2008, 2008.
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• K. Tuyls, A. Nowe, Z. Guessoum, and D. Kudenko, editors. Adaptive Agents and Multi-Agent Systems III, Lecture Notes in Artificial Intelligence, Volume 4865, Springer-Verlag, 2008.
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• Karl Tuyls and Ann Nowe. An Introduction to Game Theory. In Accepted for publication in the Encyclopedia of Computer Science and Engineering:, Wiley, 2008.
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• Karl Tuyls, Peter Vrancx, Ronald Westra, and Ann Nowe. Switching Dynamics of Multi-Agent Learning. In Accepted at the international joint conference on Autonomous Agents and Multi-Agent Systems (AAMAS'08), 2008.
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• Jaap van den Herik, Michael Kaisers, Daniel Hennes, Karl Tuyls, and Katja Verbeeck . Multi-Agent Learning Dynamics: A Survey. In Accepted at the Cooperative Information Agents Conference (CIA'07), Lecture Notes in Computer Science, Springer, Delft, The Netherlands, 2007.
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• Steven de Jong, Karl Tuyls, Katja Verbeeck, and Nico Roos . A descriptive model of priority-based fairness.. In MICC/IKAT Technical Report 01-07, ISSN 0922-8721, Maastricht, The Netherlands, 2007.
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• Thijs van Lankveld, Guido de Croon, and Karl Tuyls. Static versus Plastic Controllers in Evolutionary Robotics. In 19th Benelux Conference on Artificial Intelligence (BNAIC 2007), Utrecht, The Netherlands. Nominated for best paper award., 2007.
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• Liviu Panait and Karl Tuyls. Theoretical Advantages of Lenient Q-learners: An Evolutionary Game Theoretic Perspective. In Accepted at the Autonomous Agents and Multi-Agent Systems Conference AAMAS'07. , Honolulu, Hawai. Nominated for best paper award., 2007.
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• Karl Tuyls, Steven de Jong, Marc Ponsen, and Katja Verbeeck. Proceedings of the Seventh European Symposium on Adaptive and Learning Agents and Multi-Agent Systems (ALAMAS'07), ISSN 0922-8721, number 07-04, Maastricht University, The Netherlands, April 2nd and 3rd, 2007.
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• Karl Tuyls and Simon Parsons. What evolutionary game theory tells us about multiagent learning. Artificial Intelligence, 171(7):406–416, 2007.
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• Katja Verbeeck, Ann Nowe, Parent, J., and Karl Tuyls. Exploring Selfish Reinforcement Learning in Repeated Games with Stochastic Rewards. The Journal of Autonomous Agents and Multi-Agent Systems, 14-3:239–269, 2007.
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• Tom Croonenborghs, Karl Tuyls, Jan Ramon, and Maurice Bruynooghe. Multi-Agent Relational Reinforcement Learning. Explorations in Multi-State Coordination Tasks. In K. Tuyls, P.J. ’t Hoen, K. Verbeeck, and S. Sen, editors, Learning and Adaptation in Multi-Agent Systems, Lecture Notes in Artificial Intelligence, pp. 198–212, Springer Verlag, Berlin, 2006.
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• Bart DeVylder and Karl Tuyls. How to Reach Linguistic Consensus: A Proof of Convergence for the Naming Game. The Journal of Theoretical Biology, 242(4):818–831, 2006.
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• Pieter Jan ’t Hoen, Karl Tuyls, Liviu Panait, Sean Luke, and Johannes La Poutre. An Overview of Cooperative and Competitive Multiagent Learning. In K. Tuyls, P.J. ’t Hoen, K. Verbeeck, and S. Sen, editors, Learning and Adaptation in Multi-Agent Systems, Lecture Notes in Artificial Intelligence, pp. 1–49, Springer Verlag, Berlin, 2006.
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• Marc Ponsen, Pieter Spronck, and Karl Tuyls. Hierarchical Reinforcement Learning in Computer Games. In Proceedings of the European Symposium on Adaptive Learning Agents and Multi-Agent Systems (ALAMAS 2006), pp. 49–61, Brussels, Belgium 3-4 April, 2006.
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• Marc Ponsen, Pieter Spronck, and Karl Tuyls. Towards Relational Hierarchical Reinforcement Learning in Computer Games. In Proceedings of the 18th Benelux Conference on Artificial Intelligence (BNAIC 2006), October 5-6, Namur, Belgium, 2006.
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• Karl Tuyls, Pieter Jan 't hoen, and Bram Vanschoenwinkel. An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games. The Journal of Autonomous Agents and Multi-Agent Systems, JAAMAS, 12(1):115 – 153, 2006.
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• Karl Tuyls, Katja Verbeeck, Pieter Jan 't Hoen, and Sandip Sen, editors. Learning and Adaptation in Multi-Agent Systems, Lecture Notes in Computer Science, Volume 3898, Springer-Verlag, 2006.
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• Bart DeVylder. and Karl Tuyls. Towards a Common Lexicon in The Naming Game: The Dynamics of Synonymy Reduction. In Proceedings of BNAIC 2005, pp. 112–119, Brussels, Belgium, 2005.
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• 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.
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• Karl Tuyls, Tom Croonenborghs, Jan Ramon, Robby Goetschalckx, and Maurice Bruynooghe. Multi-Agent Relational Reinforcement Learning. In Proceedings of The Learning and Adaptation in MAS Workshop 2005 (LAMAS), held at the 4th International Conference on Autonomous Agents and Multi-Agent Systems 2005, pp. 123–132, Utrecht, the Netherlands, 2005.
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• Karl Tuyls and Bart Kuijpers. Privacy in Multi-Agent Learning: Securely Inducing a Multi-Agent Decision Tree. In Proceedings of EUMAS 2005, pp. 427–438, Brussels, Belgium, 2005.
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• Karl Tuyls and Ann Nowe. Evolutionary Game Theory and Multi-Agent Reinforcement Learning. The Knowledge Engineering Review, 20(01):63–90, 2005.
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• Karl Tuyls, Tom Lenaerts, Ann Nowe, and Bernard Manderick. An Evolutionary Game Theoretic perspective on Learning in Multi-Agent Systems. In W. van der Hoek, editors, Information, Interaction, and Agency, Springer Verlag, ISBN 1-4020-3600-0, Berlin, 2005.
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• Karl Tuyls, Katja Verbeeck, Pieter Jan 't Hoen, and Sandip Sen. Proceedings of the Learning and Adaptation in MAS workshop, held at the 4th International Conference on Autonomous Agents and Multi-Agents Systems, 2005.
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• Katja Verbeeck, Ann Nowe, and Karl Tuyls. Coordinated Exploration in Multi-Agent Reinforcement Learning: An Application to Loadbalancing. In Proceedings of the 4th International Conference on Autonomous Agents and Multi-Agent Systems, Utrecht, the Netherlands, 2005.
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• Katja Verbeeck, Maarten Peeters, Ann Nowe, and Karl Tuyls. Reinforcement Learning in Stochastic Single and Multi-Stage Games. In Daniel Kudenko, Dimitar Kazakov, and Eduardo Alonso, editors, Adaptive Agents and Multi-Agent Systems II, Lecture Notes in Artificial Intelligence, pp. 275–294, Springer Verlag, Berlin, 2005.
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• Pieter Beyens, Karl Tuyls, Kris Steenhaut, and Ann Nowe. MacCOIN: Medium Acces Control for Wireless Sensor Networks based on Collective Intelligence. In Proceedings of GTDT'04, held at the 3th International Conference on Autonomous Agents and Multi-Agent Systems, pp. 1–12, New York, USA, 2004.
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• Pieter Jan ’t Hoen and Karl Tuyls. Analyzing Multi-Agent Reinforcement Learning using Evolutionary Dynamics. In Proceedings of the 15th European Conference on Machine Learning (ECML’04), LNAI Volume 3201, Pisa, Italy, 2004.
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• 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.
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• Karl Tuyls. Learning in Multi-Agent Systems, An Evolutionary Game Theoretic Approach. Ph.D. Thesis, Vrije Universiteit Brussel, Belgium, 2004.
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• Karl Tuyls, Dries Heytens, Ann Nowe, and Bernard Manderick. Extended Replicator Dynamics as a Key to Reinforcement Learning in Multi-Agent Systems. In Proceedings of the 14th European Conference on Machine Learning (ECML’03), LNAI Volume: 2837, Cavtat-Dubrovnik, Croatia, 2003.
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• Karl Tuyls, Katja Verbeeck, and Tom Lenaerts. A Selection-Mutation model for Q-learning in Multi-Agent Systems. In Proceedings of AAMAS 2003, The ACM International Conference Proceedings Series, Melbourne, Australia, 2003.
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• Karl Tuyls, Katja Verbeeck, and and Sam Maes. On a Dynamical Analysis of Reinforcement Learning in Games: Emergence of Occam's Razor. In Lecture Notes in Artificial Intelligence, Multi-Agent Systems and Applications III, LNAI 2691, CEEMAS 2003, 16-18 june, Prague, 2003.
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• Katja Verbeeck, Ann Nowe, and Karl Tuyls. Coordinated Exploration in Stochastic Common Interest Games. In Proceedings of AAMAS-3, University of Wales, Aberystwyth, UK, 2003.
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• Karl Tuyls, Sam Maes, and and Bernard Manderick. Reinforcement Learning in Large State Spaces, Simulated Robotic Soccer as a testbed. In Lecture Notes in Artificial Intelligence, RoboCup 2002 Robot Soccer World Cup VI, LNAI 2752, 19-25 June, Fukuoka, Japan, 2002.
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• Karl Tuyls, Tom Lenaerts, Katja Verbeeck, Sam Maes, and Bernard Manderick. Towards a Relation Between Learning Agents and Evolutionary Dynamics. In Proceedings of BNAIC 2002, 21-22 October, Leuven, Belgium, 2002.
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• Karl Tuyls, Sam Maes, and Bernard Manderick. Q-Learning in Simulated Robotic Soccer, Large State Spaces and Incomplete Information. In Proceedings of ICMLA 2002, 24-27 June, Las Vegas, USA, 2002.
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• Karl Tuyls. The Dynamics of Reinforcement Learning Agents. In Poster presentation at NATO Advanced Study Institute on Learning Theory and Practice LTP 2002, 8-19 July, K.U. Leuven, Belgium, 2002.
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• Anne Defaweux, Tom Lenaerts, Sam Maes, Karl Tuyls, Piet van Remortel, Katja Verbeeck, Ann Nowe, and Bernard Manderick. Niching and Evolutionary Transitions in MAS. In Proceedings of GECCO 2001, presented at the Workshop on Evolutionary Computation and Multi-agent Systems, 7-11 July, San Francisco, USA, 2001.
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• Sam Maes, Karl Tuyls, and Bernard Manderick. Modeling a Multi-Agent Environment, Combining Influence Diagrams. In Proceedings of IAWTIC 2001, 9-11 July, Las Vegas, USA, 2001.
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• Sam Maes, Karl Tuyls, and Bernard Manderick. Combining Influence Diagrams to Model Agents. In Proceedings of ACAI'2001, 2-13 July, San Francisco, USA, 2001.
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Bioinformatics

• Jeroen Donkers and Karl Tuyls. Belief Networks for Bioinformatics. In Arpad Keleman et al., editors, Computational Intelligence in Bioinformatics, Studies in Computational Intelligence, Springer Verlag, Berlin, 2008.
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• Ben Torben-Nielsen, Karl Tuyls, and Eric Postma. EvOL Neuron: Virtual Neuron Generation. Neurocomputing, 71(4):963–972, 2008.
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• Goele Hollanders, Geert Bex, Marc Gyssens, Ronald Westra, and Karl Tuyls. On Phase Transitions in Learning Sparse Networks. In 19th Benelux Conference on Artificial Intelligence (BNAIC 2007), Utrecht, The Netherlands, 2007.
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• Ben Torben-Nielsen, Karl Tuyls, and Eric Postma. On the neuronal morphology-function relationship, a synthetic approach. In K. Tuyls, R. Westra, Y. Saeys, and A. Nowe, editors, Knowledge Discovery and Emergent Complexity in Bioinformatics, Lecture Notes in Bioinformatics, Springer Verlag, Berlin, 2007.
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• Karl Tuyls, Ronald Westra, Ann Nowe, and Yvan Saeys, editors. Knowledge Discovery and Emergent Complexity in Bioinformatics, Lecture Notes in Bioinformatics, Volume 4366, Springer-Verlag, ISBN: 978-3-540-71036-3, 2007.
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• Ronald Westra, Goele Hollanders, Geert Jan Bex, Marc Gyssens, and Karl Tuyls. The Identification of Dynamic Gene-Protein Networks. In K. Tuyls, R. Westra, Y. Saeys, and A. Nowe, editors, Knowledge Discovery and Emergent Complexity in Bioinformatics, Lecture Notes in Bioinformatics, Springer Verlag, Berlin, 2007.
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• Ronald Westra, Goele Hollanders, Geert Jan Bex, Marc Gyssens, and Karl Tuyls. The Pattern Memory of Gene-Protein Networks. AI Communications (AICOM), The European Journal on Artificial Intelligence, 20(4) :297 – 311, 2007.
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• Ann Nowe, Yvan Saeys, Karl Tuyls, Bram Vanschoenwinkel., and Ronald Westra. Proceedings of the Knowledge Discovery and Emergent Complexity in Bioninformatics workshop, Ghent University, Belgium, May 10th, 2006.
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• Ben Torben-Nielsen, Karl Tuyls, and Eric Postma. Towards Robotic Self-repair by means of Neuronal Remodelling. In Proceedings of AISB’06, Biologically Inspired Robotics (BIRO-NET), Bristol, UK, 2006.
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• Ben Torben-Nielsen, Karl Tuyls, and Eric Postma. EvOL-Neuron: method and prospects. In extended abstract, CNS Meeting, Edinburgh, UK, 2006.
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• Ben Torben-Nielsen, Karl Tuyls, and Eric Postma. An Unbiased Method for Evolutionary Optimization of Neuronal Morphologies. In Proceedings of WAM-BAMM, San Antonio, TX, USA, 2006.
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• Ronald Westra, Goele Hollanders, Ralf Peeters, and Karl Tuyls. Reconstruction of flexible gene-protein interaction networks using piecewise linear modeling and robust regression. In Proceedings of AISB’06, Network Analysis in Natural Sciences and Engineering, Bristol, UK, 2006.
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• Ronald Westra, Goele Hollanders, Ralf Peeters, and Karl Tuyls. Piecewise Linear Modeling of Gene-Protein Interaction Networks. In Proceedings of the Knowledge Discovery and Emergent Complexity workshop, May 10th, Ghent, Belgium, 2006.
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Swarm Intelligence

• Martijn ten Bhomer, Christoph Bartneck, Jun Hu, Rene Ahn, Karl Tuyls, Frank Delbressine, and Loe Feijs. . Developing Novel Extensions to Support Prototyping for Interactive Social Robots. In 21st Benelux Conference on Artificial Intelligence (BNAIC 2009), Eindhoven, The Netherlands, 2009.
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• Nyree Lemmens and Karl Tuyls. Stigmergic Landmark Foraging. In Accepted at the international joint conference on Autonomous Agents and Multi-Agent Systems (AAMAS'09), 2009.
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• Mihail Mihaylov, Karl Tuyls, and Ann Nowe . Decentralized Learning in Wireless Sensor Networks. In 21st Benelux Conference on Artificial Intelligence (BNAIC 2009), Eindhoven, The Netherlands, 2009.
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• Nyree Lemmens and Karl Tuyls. Stigmergic Landmarks Lead The Way. In Accepted at BNAIC2008, Twente, The Netherlands, 2008.
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• Steven de Jong, Karl Tuyls, Katja Verbeeck, and Nico Roos . Considerations for fairness in multi-agent systems. In Proceedings of the 7th ALAMAS Symposium, ISSN 0922-8721, number 07-04, Maastricht, The Netherlands, 2007.
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• Nyree Lemmens, Steven de Jong, Karl Tuyls, and Ann Nowe . BACO: Bee/Ant Colony Optimization. In 19th Benelux Conference on Artificial Intelligence (BNAIC 2007), Utrecht, The Netherlands, 2007.
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• Nyree Lemmens, Steven de Jong, Karl Tuyls, and Ann Nowe . Bee System with Inhibition Pheromones. In Accepted at the European Conference on Complex Systems (ECCS'07), Dresden, Germany, 2007.
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• Nyree Lemmens, Steven de Jong, Karl Tuyls, and Ann Nowe. Bee behaviour in Multi-agent Systems: A bee foraging algorithm. In Proceedings of the 7th ALAMAS Symposium, ISSN 0922-8721, number 07-04, Maastricht, The Netherlands, 2007.
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• Nyree Lemmens, Steven de Jong, Karl Tuyls, and Ann Nowe. A Bee Algorithm for Multi-Agent Systems: Recruitment and Navigation Combined. In Proceedings of ALAG 2007, an AAMAS 2007 workshop, Honolulu, Hawai, 2007.
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• Steven de Jong, Karl Tuyls, Hashimoto, T., and Lida, H. Scalable Potential-Field Multi-Agent Coordination in Resource Distribution Tasks. In In Proceedings of ALAAMAS’06, held at AAMAS'06, Hakodate, Japan, 2006.
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• Steven de Jong, Karl Tuyls, and Ida Sprinkhuizen-Kuyper. Nature-Inspired Multi-Agent Coordination in Task Assignment Problems. In Proceedings of ALAMAS 2006, The European Symposium on Adaptive Learning Agents and Multi-Agent Systems, pp. 3–13, Brussels, Belgium 3-4 April, 2006.
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• Steven de Jong, Karl Tuyls, and Ida Sprinkhuizen-Kuyper. Robust and Scalable Coordination of Potential-Field Driven Agents. In the International Conference on Intelligent Agents, Web Technologies and Internet Commerce (IAWTIC 2006), 29 November - 1 December, Sydney - Australia, 2006.
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Machine Learning - Datamining

• Goele Hollanders, Geert Jan Bex, Karl Tuyls, Marc Gyssens, and Ronald Westra . On Phase Transitions in Learning Networks. In Accepted at the 18th European Conference on Machine Learning (ECML07), Warsaw, Poland, 2007.
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• Goele Hollanders, Geert Jan Bex, Marc Gyssens, Ronald Westra, and Karl Tuyls. Learning Sparse Networks From Poor Data. In Proceedings of the 18th Benelearn Conference, Amsterdam, The Netherlands, 2007.
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• Geert Jan Bex, Frank Neven, Thomas Schwentick, and Karl Tuyls. Inference of Concise DTDs from XML data. In Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB), pp. 115–126, Seoul, Korea, 2006.
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• Geert Jan Bex, Frank Neven, Thomas Schwentick, and Karl Tuyls. Inference of Concise DTDs from XML data (Extended abstract). In Proceedings of the 18th Benelux Conference on Artificial Intelligence (BNAIC 2006), October 5-6, Namur, Belgium, 2006.
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• Ben Torben-Nielsen, Karl Tuyls, and Eric Postma. Shaping Realistic Neuronal Morphologies. A Genetic Programming Approach. In Proceedings of the International Joint Conference on Neural Networks IJCNN'2006, Vancouver, Canada, 2006.
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• Stijn Vanderlooy, Eric Postma, and Karl Tuyls. Reliable Instance Classifications in Law Enforcement. In Proceedings of the 18th Benelux Conference on Artificial Intelligence BNAIC 2006, October 5-6, Namur, Belgium, 2006.
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• Sam Maes, Karl Tuyls, Bram Vanschoenwinkel, and Bernard Manderick. Credit Card Fraud Detection, Applying Bayesian and Neural networks. In Proceedings of NF2002, 16-19 January, Havana, Cuba, 2002.
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• Karl Tuyls, Sam Maes, and Bram Vanschoenwinkel. Machine Learning Techniques for Fraud detection. Master's Thesis, Vrije Universiteit Brussel, Belgium,2000.
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Other

• Marie-Pierre Gleizes, Gal Kaminka, Ann Nowe, Sascha Ossowski, Karl Tuyls, and Katja Verbeeck. Proceedings of the Third European Workshop on Multi-Agent Systems EUMAS 2005, held at the Royal Flemish Academy of Arts and Sciences, Brussels, Belgium, 7-8 December, 2005.
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• Katja Verbeeck, Karl Tuyls, Ann Nowe, Bernard Manderick, and Bart Kuijpers. Proceedings of the Seventeenth Belgium-Netherlands Conference on Artificial Intelligence BNAIC 2005, held at the Royal Flemish Academy of Arts and Sciences, Brussels, Belgium, 17-18 October, 2005.
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