標題: Building a Player Strategy Model by Analyzing Replays of Real-Time Strategy Games
作者: Hsieh, Ji-Lung
Sun, Chuen-Tsai
資訊工程學系
Department of Computer Science
公開日期: 2008
摘要: Developing computer-controlled groups to engage in combat, control the use of limited resources, and create units and buildings in Real-Time Strategy(RTS) Games is a novel application in game AI. However, tightly controlled online commercial game pose challenges to researchers interested in observing player activities, constructing player strategy models, and developing practical AI technology in them. Instead of setting up new programming environments or building a large amount of agent's decision rules by player's experience for conducting real-time AI research, the authors use replays of the commercial RTS game StarCraft to evaluate human player behaviors and to construct an intelligent system to learn human-like decisions and behaviors. A case-based reasoning approach was applied for the purpose of training our system to learn and predict player strategies. Our analysis indicates that the proposed system is capable of learning and predicting individual player strategies, and that players provide evidence of their personal characteristics through their building construction order.
URI: http://hdl.handle.net/11536/2852
http://dx.doi.org/10.1109/IJCNN.2008.4634237
ISBN: 978-1-4244-1820-6
ISSN: 1098-7576
DOI: 10.1109/IJCNN.2008.4634237
期刊: 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8
起始頁: 3106
結束頁: 3111
Appears in Collections:Conferences Paper


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