Multi-objective adaptation of a parameterized GVGAI agent towards several games

Ahmed Khalifa, Mike Preuss, Julian Togelius

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

    Abstract

    This paper proposes a benchmark for multi-objective optimization based on video game playing. The challenge is to optimize an agent to perform well on several different games, where each objective score corresponds to the performance on a different game. The benchmark is inspired from the quest for general intelligence in the form of general game playing, and builds on the General Video Game AI (GVGAI) framework. As it is based on game-playing, this benchmark incorporates salient aspects of game-playing problems such as discontinuous feedback and a non-trivial amount of stochasticity. We argue that the proposed benchmark thus provides a different challenge from many other benchmarks for multi-objective optimization algorithms currently available. We also provide initial results on categorizing the space offered by this benchmark and applying a standard multi-objective optimization algorithm to it.

    Original languageEnglish (US)
    Title of host publicationEvolutionary Multi-Criterion Optimization - 9th International Conference, EMO 2017, Proceedings
    EditorsOliver Schütze, Gunter Rudolph, Kathrin Klamroth, Yaochu Jin, Heike Trautmann, Christian Grimme, Margaret Wiecek
    PublisherSpringer Verlag
    Pages359-374
    Number of pages16
    ISBN (Print)9783319541563
    DOIs
    StatePublished - Jan 1 2017
    Event9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 - Munster, Germany
    Duration: Mar 19 2017Mar 22 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10173 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017
    CountryGermany
    CityMunster
    Period3/19/173/22/17

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    Keywords

    • GVGAI
    • MCTS
    • Multi-objective optimization

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

    Cite this

    Khalifa, A., Preuss, M., & Togelius, J. (2017). Multi-objective adaptation of a parameterized GVGAI agent towards several games. In O. Schütze, G. Rudolph, K. Klamroth, Y. Jin, H. Trautmann, C. Grimme, & M. Wiecek (Eds.), Evolutionary Multi-Criterion Optimization - 9th International Conference, EMO 2017, Proceedings (pp. 359-374). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10173 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-54157-0_25