作者：Chaoyi Gu Varuna De Silva
评估人类的表现是许多应用程序的共同需求，例如在工程和体育领域。在评估人类在完成复杂和交互式任务时的表现时，最常见的方法是使用已被证明对该上下文有效的度量，或使用主观测量技术。然而，这可能是一个容易出错且不可靠的过程，因为静态度量无法捕捉与此类任务相关的所有复杂上下文，并且主观测量中存在偏差。我们研究的目的是创建数据驱动的人工智能代理作为计算基准，以评估人类在解决涉及多人和情境因素的困难任务时的表现。我们在足球表现分析的背景下证明了这一点。我们在大型球员和球跟踪数据集上训练了一个基于条件变分递归神经网络（VRNN）模型的生成模型。训练后的模型用于模拟两个te之间的相互作用
Evaluating the performance of human is a common need across many applications, such as in engineering and sports. When evaluating human performance in completing complex and interactive tasks, the most common way is to use a metric having been proved efficient for that context, or to use subjective measurement techniques. However, this can be an error prone and unreliable process since static metrics cannot capture all the complex contexts associated with such tasks and biases exist in subjective measurement. The objective of our research is to create data-driven AI agents as computational benchmarks to evaluate human performance in solving difficult tasks involving multiple humans and contextual factors. We demonstrate this within the context of football performance analysis. We train a generative model based on Conditional Variational Recurrent Neural Network (VRNN) Model on a large player and ball tracking dataset. The trained model is used to imitate the interactions between two teams and predict the performance from each team. Then the trained Conditional VRNN Model is used as a benchmark to evaluate team performance. The experimental results on Premier League football dataset demonstrates the usefulness of our method to existing state-of-the-art static metric used in football analytics.
论文链接：http://arxiv.org/pdf/2303.13323v1
更多计算机论文：http://cspaper.cn/