MR-TGN：用于多智能体系统中团队级意图预测的元角色时间图网络
MR-TGN: A Meta-Role Temporal Graph Network for Team-Level Intent Prediction in Multi-Agent Systems
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中文总结 AI 辅助
针对多智能体系统集体意图预测难题，提出MR-TGN框架。它将智能体建模为动态图实体，用时间记忆机制编码交互行为，引入记忆增强元角色学习机制，无需明确角色标签，实验证明该框架能有效实现早期集体意图预测。
中文摘要 AI 辅助
多智能体系统中的集体意图预测专注于预测交互智能体群体的共享目标和未来行为。该问题极具挑战性，因为集体意图源于复杂交互、不断演变的合作结构以及在动态和部分可观测环境中运行的异构智能体间的长期行为依赖。此外，智能体采用的功能角色通常是潜在的，会随时间变化，且很少有明确标注，这使得学习协调的群体行为更加困难。为应对这些挑战，本文提出了用于多智能体系统集体意图预测的元角色时间图网络（MR-TGN）框架。该框架将智能体建模为动态演变的图实体，并采用时间记忆机制来编码历史交互和协调行为。为捕获更高层次的行为知识，MR-TGN引入了一种记忆增强的元角色学习机制，可从以智能体为中心的行为表示中导出潜在角色表示，而无需明确的角色标签。还提出了一种早期集体意图预测的评估方法，以评估预测准确性和及时性，从而能够在任务执行早期对模型预测集体目标的能力进行实际评估。在具有代表性的多智能体场景上的实验结果表明，所提出的框架始终优于竞争基线，并在动态和对抗环境中实现了对集体意图的有效早期预测。
英文摘要
Collective intent prediction in multi-agent systems focuses on predicting the shared objectives and future behaviours of groups of interacting agents. The problem is particularly challenging because collective intent emerges from complex interactions, evolving cooperation structures, and long-term behavioural dependencies among heterogeneous agents operating in dynamic and partially observable environments. Furthermore, functional roles adopted by agents are often latent, may change over time, and are rarely available as explicit annotations, making the learning of coordinated group behaviours significantly more difficult. To address these challenges, this paper proposes a Meta-Role Temporal Graph Network (MR-TGN) framework for collective intent prediction in multi-agent systems. The proposed framework models agents as dynamically evolving graph entities and employs temporal memory mechanisms to encode historical interactions and coordination behaviours. To capture higher-level behavioural knowledge, MR-TGN introduces a memory-enhanced meta-role learning mechanism that derives latent role representations from agent-centric behavioral representations without requiring explicit role labels. An evaluation methodology for early collective intent prediction is proposed to assess prediction accuracy and timeliness, enabling realistic evaluation of the model's ability to anticipate collective objectives during the early stages of mission execution. Experimental results on representative multi-agent scenarios demonstrate that the proposed framework consistently outperforms competitive baselines and achieves effective early prediction of collective intents in dynamic and adversarial environments.
