Generating diverse opponents with multiobjective evolution

Alexandros Agapitos, Julian Togelius, Simon M. Lucas, Jürgen Schmidhuber, Andreas Konstantinidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

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 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. ©2008 IEEE.
Original languageEnglish (US)
Title of host publication2008 IEEE Symposium on Computational Intelligence and Games, CIG 2008
Pages135-142
Number of pages8
DOIs
StatePublished - Dec 1 2008
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

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