...结构 对手建模[gap=767]ulti-agent learning; Q-learning; profit-sharing learning ; modular architecture; opponent modeling...
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In opponent modeling, we propose a creative method to use non-linear neural networks to recognize opponent’s strategy and formation, which greatly improve agent’s intelligence and match scores.
在对手建模中，创造性的提出了用非线性的人工神经网络来对对手的阵型与战术做辨识，这将极大的提高Agent的智能与球队的比赛成绩。
参考来源 - RoboCup仿真机器人足球赛研究·2,447,543篇论文数据，部分数据来源于NoteExpress
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