[Apologies if you receive multiple copies of this CFP] *Call for Papers - Special Session on Games* *The 2019 IEEE Congress on Evolutionary Computation (IEEE CEC2019)* *Wellington, New Zealand, 10-13 June 2019* *http://cec2019.org/programs/special_sessions.html#cec-04 <http://cec2019.org/programs/special_sessions.html#cec-04>*
*Important Dates* *Extended submission deadline: 21 January 2019* Notification: 7 March 2019 Final paper submission: 31 March 2019 *Please select "CEC-04: Special Session on Games" when submitting your paper.* **This special session is organized in association with the **IEEE CIS Computational Intelligence Society Technical Committee on Games (Game TC)* *.* Games are an ideal domain to study computational intelligence (CI) methods because they provide affordable, competitive, dynamic, reproducible environments suitable for testing new search algorithms, pattern-based evaluation methods, or learning concepts. Games scale from simple problems for developing algorithms to incredibly hard problems for testing algorithms to the limit. They are also interesting to observe, fun to play, and very attractive to students. Additionally, there is great potential for CI methods to improve the design and development of both computer games as well as tabletop games, board games, and puzzles. This special session aims at gathering leaders and neophytes in games research as well as practitioners in this field who research applications of computational intelligence methods to computer games. *Topics* In general, papers are welcome that consider all kinds of applications of methods (evolutionary computation, supervised learning, unsupervised learning, fuzzy systems, game-tree search, rolling horizon algorithms, MCTS, etc.) to games (card games, board games, mathematical games, action games, strategy games, role-playing games, arcade games, serious games, etc.). Examples include but are not limited to: - Adaptation in games - Automatic game testing - Coevolution in games - Comparative studies (e.g. CI versus human-designed players) - Dynamic difficulty in games - Games as test-beds for algorithms - Imitating human players - Learning to play games - Multi-agent and multi-strategy learning - Player/Opponent modelling - Procedural content generation - CI for Serious Games (e.g., games for health care, education or training) - Results of game-based CI and open competitions *Organizers* *Jialin Liu, liujl(at)sustc.edu.cn <http://sustc.edu.cn/>* Research Assistant Professor, Dept. of Computer Science and Engineering, Southern University of Science and Technology, China *Daniel Ashlock, dashlock(at)uoguelph.ca <http://uoguelph.ca/>* Professor, Dept. of Mathematics and Statistics, University of Guelph, Canada
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