提出一套协作度量体系，分析异构多智能体系统中的合作效率与依赖关系。
Beyond Task Performance: A Metric-Based Analysis of Sequential Cooperation in Heterogeneous Multi-Agent Destructive Foraging
- 设计三类通用协作度量：团队间、团队内及基础指标，全面刻画合作行为。
- 在动态水面清洁仿真中验证，可区分学习算法与启发式方法的协作差异。
- 适合研究多智能体协作机制、任务分工与公平性的研究人员参考。
本文针对部分可观测且存在时序角色依赖的异构多智能体系统中的协作分析问题，提出一套通用的协作度量体系。不同于以往仅关注任务完成率的研究，该体系旨在刻画效率、协调性、团队间依赖、公平性与敏感性等多维度合作特征。度量分为三大类：基础指标、团队间指标与团队内指标，适用于类似觅食任务的多智能体序列场景。在受真实水域清洁任务启发的破坏性觅食场景中进行了验证，涉及两个具有先后依赖关系的专业化团队——一个负责资源搜索，另一个负责销毁。评估了多种代表性方法，涵盖基于学习的算法与经典启发式策略。
原文摘要 · Abstract (English)
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.
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