从对抗性多智能体轨迹学习预测篮球投篮
Predicting Shot Making in Basketball Learnt from Adversarial Multiagent Trajectories
摘要：
在本文中，我们预测的可能性，一个球员在篮球多智能体轨迹投篮。以前解决类似问题的方法主要集中在手工制作功能来捕获特定领域的知识。虽然直观，但最近的深度学习工作表明，这种方法容易丢失重要的预测特征。为了解决这个问题，我们提出了一种卷积神经网络（CNN）方法，我们最初将多智能体行为表示为图像。为了编码篮球的对抗性，我们使用多通道图像，然后将其输入CNN。此外，为了捕捉轨迹的时间方面，我们“淡化”玩家轨迹。我们发现，这种方法是优于传统的FFN模型。通过使用梯度上升来使用已经训练好的CNN创建图像，我们发现了CNN过滤器学习的特征。最后，我们发现组合的CNN+FFN是性能最好的网络，错误率为39%。
英文摘要：
In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain specific knowledge. Although intuitive, recent work in deep learning has shown this approach is prone to missing important predictive features. To circumvent this issue, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multi-channel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories we "fade" the player trajectories. We find that this approach is superior to a traditional FFN model. By using gradient ascent to create images using an already trained CNN, we discover what features the CNN filters learn. Last, we find that a combined CNN+FFN is the best performing network with an error rate of 39%.
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。
