Robust player imitation using multiobjective evolution

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

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

    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.

    Original languageEnglish (US)
    Title of host publication2009 IEEE Congress on Evolutionary Computation, CEC 2009
    Pages652-659
    Number of pages8
    DOIs
    StatePublished - 2009
    Event2009 IEEE Congress on Evolutionary Computation, CEC 2009 - Trondheim, Norway
    Duration: May 18 2009May 21 2009

    Publication series

    Name2009 IEEE Congress on Evolutionary Computation, CEC 2009

    Other

    Other2009 IEEE Congress on Evolutionary Computation, CEC 2009
    CountryNorway
    CityTrondheim
    Period5/18/095/21/09

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computational Theory and Mathematics
    • Theoretical Computer Science

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