Beyond Task Performance: A Metric-Based Analysis of Sequential Cooperation in Heterogeneous Multi-Agent Destructive Foraging
🤖 AI Summary
Existing research on heterogeneous multi-agent systems with partial observability and temporal role dependencies has predominantly emphasized task performance while lacking systematic metrics to capture the essence of cooperation—such as coordination, fairness, and interdependence. This work proposes a generalizable, multi-level cooperation measurement framework that, for the first time, systematically quantifies collaborative behaviors among heterogeneous agents in temporally dependent tasks. The framework encompasses three transferable categories of metrics: efficiency, intra- and inter-team coordination, and fairness with sensitivity. Leveraging this metric-driven evaluation approach, we validate collaborative dynamics in two types of specialized heterogeneous teams within a simulated water-cleaning scenario, combining both learning-based and heuristic algorithms. Empirical results demonstrate that the proposed metrics effectively differentiate the quality of collaboration across algorithms, overcoming the limitations of conventional evaluations that rely solely on task success rates.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
heterogeneous multi-agent systems
sequential cooperation
partial observability
temporal role dependency
destructive foraging
Innovation
Methods, ideas, or system contributions that make the work stand out.
cooperation metrics
heterogeneous multi-agent systems
sequential cooperation
destructive foraging
partial observability
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