Generating Diverse Opponents with Multiobjective Evolution

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@InProceedings{Agapitos:2008:CIG,

• author = "Alexandros Agapitos and Julian Togelius and Simon M. Lucas and Jurgen Schmidhuber and Andreas Konstantinidis",
• title = "Generating Diverse Opponents with Multiobjective Evolution",
• booktitle = "Proceedings of the 2008 IEEE Symposium on Computational Intelligence and Games",
• year = "2008",
• pages = "135--142",
• address = "Perth, Australia",
• month = dec # " 15-18",
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Reinforcement Learning, Multiobjective Evolution, AI in Computer Games, EMOA, Car Racing, MOGA, AI game agent, computational intelligence, diverse opponent generation, game play learning, multiobjective evolutionary algorithm, nonplayer character, computer games, evolutionary computation, learning (artificial intelligence), multi-agent systems",
• URL = "http://julian.togelius.com/Agapitos2008Generating.pdf",
• DOI = "doi:10.1109/CIG.2008.5035632",
• abstract = "For computational intelligence to be useful in creating game agent AI, we need to focus on creating interesting and believable agents rather than just learn to play the games well. To this end, we propose a way to use multiobjective evolutionary algorithms to automatically create populations of NPCs, such as opponents and collaborators, that are interestingly diverse in behaviour space. Experiments are presented where a number of partially conflicting objectives are defined for racing game competitors, and multiobjective evolution of GP-based controllers yield Pareto fronts of interesting controllers.",
• notes = "Also known as \cite{5035632}",

}

Genetic Programming entries for Alexandros Agapitos Julian Togelius Simon M Lucas Jurgen Schmidhuber Andreas Constantinides

Citations