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 based Adversarial Modeling with Mutal Information (GrAMMI) for modeling the behavior of an adversarial opponent agent. GrAMMI 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 GrAMMI, 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, GrAMMI 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：在职业体育、视频游戏设计以及毒品贩运拦截等现实场景中，对手建模与跟踪的需求普遍存在。本研究提出基于图的互信息对抗性建模方法（GrAMMI），用于对对抗性对手代理的行为进行建模。GrAMMI是一种基于图神经网络（GNN）的创新方法，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性对手的当前状态与未来状态。为评估GrAMMI性能，我们设计了两个受现实场景启发的大规模追逃域：异构代理团队需在此类域中追踪并拦截单个对抗性代理，而该对抗性代理则需在实现自身目标的同时规避检测。基于互信息公式，GrAMMI在两个域中均优于所有基线方法，其未来对抗性状态预测的对数似然值在两个域中平均提升31.68%。