Soccer Simulation 2D (SS2D) is a simulation of a real soccer game in two dimensions. In soccer, passing behavior is an essential action for keeping the ball in possession of our team and creating goal opportunities. Similarly, for SS2D, predicting the passing behaviors of both opponents and our teammates helps manage resources and score more goals. Therefore, in this research, we have tried to address the modeling of passing behavior of soccer 2D players using Deep Neural Networks (DNN) and Random Forest (RF). We propose an embedded data extraction module that can record the decision-making of agents in an online format. Afterward, we apply four data sorting techniques for training data preparation. After, we evaluate the trained models' performance playing against 6 top teams of RoboCup 2019 that have distinctive playing strategies. Finally, we examine the importance of different feature groups on the prediction of a passing strategy. All results in each step of this work prove our suggested methodology's effectiveness and improve the performance of the pass prediction in Soccer Simulation 2D games ranging from 5\% (e.g., playing against the same team) to 10\% (e.g., playing against Robocup top teams).

翻译：足球仿真2D（SS2D）是对真实足球比赛的二维模拟。在足球运动中，传球行为是保持球队控球权并创造得分机会的关键动作。类似地，在SS2D中，预测对手和队友的传球行为有助于管理资源并取得更多进球。因此，本研究尝试利用深度神经网络（DNN）和随机森林（RF）对足球2D球员的传球行为进行建模。我们提出一种嵌入式数据提取模块，能够以在线格式记录智能体的决策过程。随后，我们应用四种数据排序技术进行训练数据准备。接着，我们评估训练模型与RoboCup 2019中6支具有独特比赛策略的顶级球队对抗时的性能。最后，我们分析不同特征组对传球策略预测的重要性。本工作各步骤的结果均证明了所提方法的有效性，并将足球仿真2D比赛中的传球预测性能提升了5%（如对阵同一球队）至10%（如对阵RoboCup顶级球队）。