运动比赛中球员位置的推断：基于有限观测的多智能体空间插补
Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations
摘要：
理解多智能体系统（MAS）中的智能体行为是自动驾驶、灾难响应和体育分析等领域的一个重要问题。现有的MAS问题通常使用统一的时间步长与所有代理的意见。在这项工作中，我们分析了代理位置插补问题，特别是在非均匀时间步长和有限的代理可观察性（约95%缺失值）的环境中。我们的方法使用长短期记忆和图神经网络组件来学习时间和代理间模式，以预测所有代理在每个时间步的位置。我们通过从稀疏事件数据（例如，射击和传球）。我们的模型估计玩家的位置在~ 6.9米之内;从最佳表现的基线来看，误差减少了~62%。这种方法有助于下游分析任务，例如球员身体指标，球员覆盖率和球队球场控制。这些任务的现有解决方案通常需要光学跟踪数据，这是昂贵的，只有精英俱乐部才能获得。通过从容易获得的事件数据中输入玩家位置，我们增加了下游任务的可访问性。
英文摘要：
Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS problems typically use uniform timesteps with observations for all agents. In this work, we analyse the problem of agent location imputation, specifically posed in environments with non-uniform timesteps and limited agent observability (~95% missing values). Our approach uses Long Short-Term Memory and Graph Neural Network components to learn temporal and inter-agent patterns to predict the location of all agents at every timestep. We apply this to the domain of football (soccer) by imputing the location of all players in a game from sparse event data (e.g., shots and passes). Our model estimates player locations to within ~6.9m; a ~62% reduction in error from the best performing baseline. This approach facilitates downstream analysis tasks such as player physical metrics, player coverage, and team pitch control. Existing solutions to these tasks often require optical tracking data, which is expensive to obtain and only available to elite clubs. By imputing player locations from easy to obtain event data, we increase the accessibility of downstream tasks.
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。
