Liviu Panait received a Ph.D. degree in Computer Science from George Mason University in 2007, and is currently working on collecting the world's information and making it universally accessible and useful. His research interests include machine learning, multiagent systems, computer games, artificial life, data mining, and information retrieval.
Liviu Panait co-chaired the AAMAS 2007 Workshop on Adaptive and Learning Agents, the AAMAS 2006 Workshop on Adaptation and Learning in Autonomous Agents and Multiagent Systems, co-organized the AAAI 2005 Fall Symposium on Coevolutionary and Coadaptive Systems, served as a program committee member or as an invited reviewer for multiple international conferences and journals, and he is a member of the IEEE Task Force on Coevolution. He is a co-author of the ECJ evolutionary computation library and the MASON multi-agent simulation toolkit. For more information, please visit his home page.
Cooperative Coevolution and Univariate Estimation of Distribution Algorithms, Christopher Vo, Liviu Panait, Sean Luke, Foundations of Genetic Algorithms, 2009.
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective, Liviu Panait, Karl Tuyls, Sean Luke, Journal of Machine Learning Research (2008).
Theoretical Advantages of Lenient Learners in Multiagent Systems, Liviu Panait, Karl Tuyls, Proceedings of the Sixth International Conference on Autonomous Agents and Multi-agent Systems (AAMAS-07), 2007.