Supervised Learning in Football Game Environments Using Artificial Neural Networks

Date

2018-09-23

Author

Baykal, Ömer
Alpaslan, Ferda Nur

Metadata

Show full item record

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

Item Usage Stats

254
views

0
downloads

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

Suggestions

OpenMETU
Core

Agent learning in fully observable, continuous and real-time game environments Baykal, Ömer; Alpaslan, Ferda Nur; Department of Computer Engineering (2016) Game industry has become one of the sectors that commonly use artificial intelli- gence. Today, most of the game environments need and include artificial intelligence agents to offer more challenging and entertaining experience. Development processes and the quality of artificial intelligence agents are the most important concerns in this area. Since it becomes harder to develop good agents as games become more com- plex, machine learning methods have started to be used in some notable games to shorten this...
Reinforcement Learning in Card Game Environments Using Monte Carlo Methods and Artificial Neural Networks Baykal, Ömer; Alpaslan, Ferda Nur (2019-09-01) Artificial intelligence has wide range of application areas and games are one of the important ones. There are many applications of artificial intelligence methods in game environments. It is very common for game environments to include intelligent agents. Having intelligent agents makes a game more entertaining and challenging for its players. Reinforcement learning methods can be applied to develop artificial intelligence agents that learn to play a game by themselves without any supervision and can play ...
Learning to play an imperfect information card game using reinforcement learning Alpaslan, Ferda Nur; Baykal, Ömer; Demirdöver, Buğra Kaan (2022-08-01) Artificial intelligence and machine learning are widely popular in many areas. One of the most popular ones is gaming. Games are perfect testbeds for machine learning and artificial intelligence with various scenarios and types. This study aims to develop a self-learning intelligent agent to play the Hearts game. Hearts is one of the most popular trick-taking card games around the world. It is an imperfect information card game. In addition to having a huge state space, Hearts offers many extra challenges d...
Case studies on the use of neural networks in eutrophication modeling Karul, C; Soyupak, S; Cilesiz, AF; Akbay, N; Germen, E (2000-10-30) Artificial neural networks are becoming more and more common to be used in development of prediction models for complex systems as the theory behind them develops and the processing power of computers increase. A three layer Levenberg-Marquardt feedforward learning algorithm was used to model the eutrophication process in three water bodies of Turkey (Keban Dam Reservoir, Mogan and Eymir Lakes). Despite the very complex and peculiar nature of Keban Dam, a relatively good correlation (correlation coefficient...
Mesh segmentation from sparse face labels using graph convolutional neural networks. Sever, Önder İlke; Sahillioğlu, Yusuf; Department of Computer Engineering (2020) The marked improvements in deep learning influence almost every area of computer science. The mesh segmentation problem in computer graphics has been an active research area and keep abreast of the trend of deep learning developments. The mesh segmentation has a central role in multiple application areas for 3D objects. It is chiefly used to produce the object structure in order to manipulate the object or analyze the components of it. These operations are primitive, and that primitiveness causes a variety ...

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.