Self-organizing neural networks for behavior modeling in games
This paper proposes self-organizing neural networks for modeling behavior of non-player characters (NPC) in first person shooting games. Specifically, two classes of self-organizing neural models, namely Self-Generating Neural Networks (SGNN) and Fusion Architecture for Learning and Cognition (FALCO...
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sg-smu-ink.sis_research-78032022-01-27T08:33:21Z Self-organizing neural networks for behavior modeling in games FENG, Shu TAN, Ah-hwee This paper proposes self-organizing neural networks for modeling behavior of non-player characters (NPC) in first person shooting games. Specifically, two classes of self-organizing neural models, namely Self-Generating Neural Networks (SGNN) and Fusion Architecture for Learning and Cognition (FALCON) are used to learn non-player characters' behavior rules according to recorded patterns. Behavior learning abilities of these two models are investigated by learning specific sample Bots in the Unreal Tournament game in a supervised manner. Our empirical experiments demonstrate that both SGNN and FALCON are able to recognize important behavior patterns and learn the necessary knowledge to operate in the Unreal environment. Comparing with SGNN, FALCON is more effective in behavior learning, in terms of lower complexity and higher fighting competency 2010-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6800 info:doi/10.1109/IJCNN.2010.5596471 https://ink.library.smu.edu.sg/context/sis_research/article/7803/viewcontent/NPC_IJCNN_2010.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems |
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Databases and Information Systems FENG, Shu TAN, Ah-hwee Self-organizing neural networks for behavior modeling in games |
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This paper proposes self-organizing neural networks for modeling behavior of non-player characters (NPC) in first person shooting games. Specifically, two classes of self-organizing neural models, namely Self-Generating Neural Networks (SGNN) and Fusion Architecture for Learning and Cognition (FALCON) are used to learn non-player characters' behavior rules according to recorded patterns. Behavior learning abilities of these two models are investigated by learning specific sample Bots in the Unreal Tournament game in a supervised manner. Our empirical experiments demonstrate that both SGNN and FALCON are able to recognize important behavior patterns and learn the necessary knowledge to operate in the Unreal environment. Comparing with SGNN, FALCON is more effective in behavior learning, in terms of lower complexity and higher fighting competency |
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text |
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FENG, Shu TAN, Ah-hwee |
author_facet |
FENG, Shu TAN, Ah-hwee |
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FENG, Shu |
title |
Self-organizing neural networks for behavior modeling in games |
title_short |
Self-organizing neural networks for behavior modeling in games |
title_full |
Self-organizing neural networks for behavior modeling in games |
title_fullStr |
Self-organizing neural networks for behavior modeling in games |
title_full_unstemmed |
Self-organizing neural networks for behavior modeling in games |
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self-organizing neural networks for behavior modeling in games |
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Institutional Knowledge at Singapore Management University |
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2010 |
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https://ink.library.smu.edu.sg/sis_research/6800 https://ink.library.smu.edu.sg/context/sis_research/article/7803/viewcontent/NPC_IJCNN_2010.pdf |
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