The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction. In this work, we present graPh neurAl Network aDvErsarial MOdeliNg wIth mUtual informMation for modeling the behavior of an adversarial opponent agent. PANDEMONIUM is a novel graph neural network (GNN) based approach that uses mutual information maximization as an auxiliary objective to predict the current and future states of an adversarial opponent with partial observability. To evaluate PANDEMONIUM, we design two large-scale, pursuit-evasion domains inspired by real-world scenarios, where a team of heterogeneous agents is tasked with tracking and interdicting a single adversarial agent, and the adversarial agent must evade detection while achieving its own objectives. With the mutual information formulation, PANDEMONIUM outperforms all baselines in both domains and achieves 31.68% higher log-likelihood on average for future adversarial state predictions across both domains.

翻译：对手建模与追踪需求出现在多个现实场景中，例如职业体育、视频游戏设计以及毒品走私拦截。本研究提出一种基于相互信息的图神经网络对抗建模方法（PANDEMONIUM），用于对对抗性对手智能体的行为进行建模。PANDEMONIUM是一种基于图神经网络（GNN）的新型方法，通过将互信息最大化作为辅助目标，在部分可观测条件下预测对抗对手的当前及未来状态。为评估PANDEMONIUM，我们设计了两个受现实场景启发的大规模追捕-逃逸领域，其中一组异构智能体需追踪并拦截单个对抗智能体，而该对抗智能体需在实现自身目标的同时规避检测。通过互信息公式化，PANDEMONIUM在两个领域的表现均优于所有基线方法，在未来对抗状态预测任务中平均对数似然率提升31.68%。