Supervised Learning in Football Game Environments Using Artificial Neural Networks

Date

2018-09-23

Author

Baykal, Ömer
Alpaslan, Ferda Nur

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Game industry has become one of the sectors that commonly use artificial intelligence. Today, most of the game environments include artificial intelligence agents to offer more challenging and entertaining gameplay experience. Since it gets harder to develop good agents as games become more complex, machine learning methods have started to be used in some notable games to shorten the development process of agents and to improve their quality. Popularity of machine learning applications in game environments has increased in the last decades. Supervised learning methods are applied to develop artificial intelligence agents that play a game like human players by imitating them. The imitating agents can either play the role of opponents or play on behalf of the real players when they are absent. The purpose of this study is to develop imitating agents fir a popular online game, namely HaxBall. ilaxBall is a two dimensional football game with fully observable, continuous, and real-time game environment.

Subject Keywords

Games, Feedforward neural networks, Neural networks, Supervised learning, Learning systems, Artificial intelligence

URI

https://hdl.handle.net/11511/35809

DOI

https://doi.org/10.1109/ubmk.2018.8566428

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

Ö. Baykal and F. N. Alpaslan, “Supervised Learning in Football Game Environments Using Artificial Neural Networks,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35809.