TY - GEN
T1 - Generating diverse opponents with multiobjective evolution
AU - Agapitos, Alexandros
AU - Togelius, Julian
AU - Lucas, Simon M.
AU - Schmidhuber, Jürgen
AU - Konstantinidis, Andreas
PY - 2008
Y1 - 2008
N2 - 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 use multiobjective evolutionary algorithms to automatically create populations of Non-Player Characters (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 Genetic Programming-based controllers yield pareto fronts of interesting controllers.
AB - 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 use multiobjective evolutionary algorithms to automatically create populations of Non-Player Characters (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 Genetic Programming-based controllers yield pareto fronts of interesting controllers.
UR - http://www.scopus.com/inward/record.url?scp=70349300413&partnerID=8YFLogxK
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U2 - 10.1109/CIG.2008.5035632
DO - 10.1109/CIG.2008.5035632
M3 - Conference contribution
AN - SCOPUS:70349300413
SN - 9781424429745
T3 - 2008 IEEE Symposium on Computational Intelligence and Games, CIG 2008
SP - 135
EP - 142
BT - 2008 IEEE Symposium on Computational Intelligence and Games, CIG 2008
T2 - 2008 IEEE Symposium on Computational Intelligence and Games, CIG 2008
Y2 - 15 December 2008 through 18 December 2008
ER -