A Convex Formulation of Game-Theoretic Hierarchical Routing

Hierarchical decision-making is a natural paradigm for coordinating multi-agent systems in complex environments such as air traffic management. In this paper, we present a bilevel framework for game-theoretic hierarchical routing, where a high-level router assigns discrete routes to multiple vehicles who seek to optimize potentially noncooperative objectives that depend upon the assigned routes. To address computational challenges, we propose a reformulation that preserves the convexity of each agent’s feasible set. This convex reformulation enables a solution to be identified efficiently via a branch-and-bound algorithm. Our approach ensures global optimality while capturing strategic interactions between agents at the lower level. We demonstrate the solution concept of our framework in two-vehicle and three-vehicle routing scenarios.

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Publication Details

Journal
IEEE Control Systems Letters
Published
2025-01-01
DOI
https://doi.org/10.1109/lcsys.2025.3571655
Primary Topic
Mobile Ad Hoc Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

A Convex Formulation of Game-Theoretic Hierarchical Routing

David Fridovich-Keil, Max Z. Li, Dong Ho Lee, Kaitlyn Donnel
IEEE Control Systems Letters
Mobile Ad Hoc Networks
article

A Convex Formulation of Game-Theoretic Hierarchical Routing

David Fridovich-Keil, Max Z. Li, Dong Ho Lee, Kaitlyn Donnel
article en

Abstract

Hierarchical decision-making is a natural paradigm for coordinating multi-agent systems in complex environments such as air traffic management. In this paper, we present a bilevel framework for game-theoretic hierarchical routing, where a high-level router assigns discrete routes to multiple vehicles who seek to optimize potentially noncooperative objectives that depend upon the assigned routes. To address computational challenges, we propose a reformulation that preserves the convexity of each agent’s feasible set. This convex reformulation enables a solution to be identified efficiently via a branch-and-bound algorithm. Our approach ensures global optimality while capturing strategic interactions between agents at the lower level. We demonstrate the solution concept of our framework in two-vehicle and three-vehicle routing scenarios.

IEEE Control Systems LettersVol. 9
University of Michigan (US), The University of Texas at Austin (US)
National Science Foundation
Openalex Percentile: Top 89%
Mobile Ad Hoc Networks
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