Robust player imitation using multiobjective evolution

Niels Van Hoorn, Julian Togelius, Daan Wierstra, Jürgen Schmidhuber

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

39 Scopus citations

Abstract

The problem of how to create NPC AI for videogames that believably imitates particular human players is addressed. Previous approaches to learning player behaviour is found to either not generalize well to new environments and noisy perceptions, or to not reproduce human behaviour in sufficient detail. It is proposed that better solutions to this problem can be built on multiobjective evolutionary algorithms, with objectives relating both to traditional progress-based fitness (playing the game well) and similarity to recorded human behaviour (behaving like the recorded player). This idea is explored in the context of a modern racing game.© 2009 IEEE.
Original languageEnglish (US)
Title of host publication2009 IEEE Congress on Evolutionary Computation, CEC 2009
Pages652-659
Number of pages8
DOIs
StatePublished - Nov 25 2009
Externally publishedYes

Bibliographical note

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

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