This work addresses the problem of analyzing cooperation in heterogeneous multi-agent systems which operate under partial observability and temporal role dependency, framed within a destructive multi-agent foraging setting. Unlike most previous studies, which focus primarily on algorithmic performance with respect to task completion, this article proposes a systematic set of general-purpose cooperation metrics aimed at characterizing not only efficiency, but also coordination and dependency between teams and agents, fairness, and sensitivity. These metrics are designed to be transferable to different multi-agent sequential domains similar to foraging. The proposed suite of metrics is structured into three main categories that jointly provide a multilevel characterization of cooperation: primary metrics, inter-team metrics, and intra-team metrics. They have been validated in a realistic destructive foraging scenario inspired by dynamic aquatic surface cleaning using heterogeneous autonomous vehicles. It involves two specialized teams with sequential dependencies: one focused on the search of resources, and another on their destruction. Several representative approaches have been evaluated, covering both learning-based algorithms and classical heuristic paradigms.

翻译：本研究针对异构多智能体系统在部分可观测性与时序角色依赖性条件下的合作分析问题，以破坏性多智能体觅食为框架展开。与以往多数研究主要关注任务完成度的算法性能不同，本文提出了一套系统化的通用合作度量指标，旨在不仅刻画效率，同时表征团队与智能体间的协调性、依赖性、公平性及敏感性。这些度量指标被设计为可迁移至类似觅食的其他多智能体序列决策领域。所提出的度量体系分为三个主要类别，共同提供多层次的合作表征：基础度量、团队间度量与团队内度量。该体系已在受异构自主水面载具动态清洁任务启发的现实破坏性觅食场景中得到验证。该场景涉及两个具有时序依赖性的专业化团队：一个专注于资源搜寻，另一个负责资源销毁。研究评估了若干代表性方法，涵盖基于学习的算法与经典启发式范式。