工程化特征以改进 Soccer Simulation 2D 比赛中的传球预测
Engineering Features to Improve Pass Prediction in Soccer Simulation 2D Games
AI总结：
本研究提出基于 DNN 和 RF 模型及嵌入式数据提取模块，预测 SS2D 比赛中传球行为，经对阵 RoboCup 2019 顶尖队伍验证，将传球预测性能提升 5% 至 10%。
摘要：
Soccer Simulation 2D (SS2D) 是真实足球比赛的二维仿真。在足球中，传球行为是保持我方控球并创造进球机会的关键动作。同样地，对于 SS2D，预测对手和队友的传球行为有助于管理资源并进更多球。因此，在本研究中，我们尝试使用深度神经网络 (DNN) 和随机森林 (RF) 来对 Soccer 2D 球员的传球行为进行建模。我们提出了一个嵌入式数据提取模块，能够以在线格式记录智能体的决策。随后，我们应用四种数据排序技术进行训练数据准备。接着，我们评估了训练好的模型与 RoboCup 2019 中 6 支具有独特比赛策略的顶尖队伍对阵时的表现。最后，我们考察了不同特征组对传球策略预测的重要性。本工作每个步骤的所有结果都证明了我们提出方法的有效性，并将 Soccer Simulation 2D 比赛中的传球预测性能提升了 5%（例如，与同队比赛）到 10%（例如，与 Robocup 顶尖队伍比赛）。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
