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Machine learning of character behavior in computer games

David Penca (2018) Machine learning of character behavior in computer games. EngD thesis.

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    In our thesis we present an approach for programming enemy characters in online multiplayer games that is based on machine learning algorithms. We wish to demonstrate, that it is possible to specify the available actions for specific characters, implement sensing of their environment and let them learn the tactics on their own, by fighting human players. Approaches based on machine learning have the potential to reduce the time needed for programming as well as enable the characters to adapt to current player tactics, without any additional programming. By using such programming methods we are able to create characters which get better over time and are not vulnerable to exploitation of established tactics by the players. We have focused mainly on reinforcement learning and evolutionary algorithms, because both approaches are suitable for use in systems that learn from numerous interactions with human players. We have implemented our prototype in the Unreal Engine 4 game engine.

    Item Type: Thesis (EngD thesis)
    Keywords: machine learning, Q-learning, genetic algorithms, computer games, inteligent agents.
    Number of Pages: 30
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    izr. prof. dr. Zoran Bosnić3826Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537966019)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 4288
    Date Deposited: 08 Oct 2018 17:12
    Last Modified: 10 Oct 2018 11:29
    URI: http://eprints.fri.uni-lj.si/id/eprint/4288

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