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Meme representations for game agents

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Abstract

The advancement in game technology has served to enrich player’s gaming experience in a substantial way. Nowadays, it is common to have blockbuster quality games, with realistic graphics and engaging stories. Despite this, the progress made in incorporating Artificial Intelligence has been slow, and realistic human-like intelligence in games is hardly to be found. There have been some attempts to use Machine Learning in games, but such attempts often ended up impractical or affecting the players enjoyment due to several constraining factors. In this paper, we describe Meme War as a proof-of-concept for practical usage of Machine Learning in games. We introduce Extreme Learning Machine (ELM) as one approach to achieve a better experience in employing Machine Learning in games. Advantages of ELM over Multilayer Perceptron (MLP) are presented in terms of what ELM can offer in a practical point of view of the player.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore

    Yoan Miche & Meng-Hiot Lim

  2. School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore

    Yew-Soon Ong

  3. Department of Information and Computer Science, Aalto University School of Science, FI-00076, Aalto, Finland

    Yoan Miche & Amaury Lendasse

  4. IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain

    Amaury Lendasse

  5. Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, Paseo Manuel Lardizabal 1, Donostia/San Sebastian, Spain

    Amaury Lendasse

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  1. Yoan Miche
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  2. Meng-Hiot Lim
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  3. Amaury Lendasse
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  4. Yew-Soon Ong
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Correspondence to Yoan Miche.

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Miche, Y., Lim, MH., Lendasse, A. et al. Meme representations for game agents. World Wide Web 18, 215–234 (2015). https://doi.org/10.1007/s11280-013-0219-3

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  • DOI: https://doi.org/10.1007/s11280-013-0219-3

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