A Taxonomy of Hierarchical Multi-Agent Systems: Design Patterns, Coordination Mechanisms, and Industrial Applications
David J. Moore

TL;DR
This paper introduces a comprehensive taxonomy for hierarchical multi-agent systems, connecting structural, temporal, and communication aspects to facilitate comparison, design, and application in industrial contexts, while highlighting open challenges.
Contribution
It presents the first unified taxonomy integrating multiple dimensions of HMAS, linking classical and modern coordination mechanisms within a single framework.
Findings
Hierarchical structures can improve global efficiency and local autonomy.
The taxonomy aids in comparing and designing HMAS across different applications.
Open challenges include explainability, scalability, and safe integration of learning agents.
Abstract
Hierarchical multi-agent systems (HMAS) organize collections of agents into layered structures that help manage complexity and scale. These hierarchies can simplify coordination, but they also can introduce trade-offs that are not always obvious. This paper proposes a multi-dimensional taxonomy for HMAS along five axes: control hierarchy, information flow, role and task delegation, temporal layering, and communication structure. The intent is not to prescribe a single "best" design but to provide a lens for comparing different approaches. Rather than treating these dimensions in isolation, the taxonomy is connected to concrete coordination mechanisms - from the long-standing contract-net protocol for task allocation to more recent work in hierarchical reinforcement learning. Industrial contexts illustrate the framework, including power grids and oilfield operations, where agents at…
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Taxonomy
TopicsScheduling and Optimization Algorithms
