Provably Stable Multi-Agent Routing with Bounded-Delay Adversaries in the Decision Loop

Roee M. Francos; Daniel Garces; Stephanie Gil

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arXiv · 2504.00863

Provably Stable Multi-Agent Routing with Bounded-Delay Adversaries in the Decision Loop

Roee M. Francos·Daniel Garces·Stephanie Gil

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Abstract

In this work, we are interested in studying multi-agent routing settings, where adversarial agents are part of the assignment and decision loop, degrading the performance of the fleet by incurring bounded delays while servicing pickup-and-delivery requests. Specifically, we are interested in characterizing conditions on the fleet size and the proportion of adversarial agents for which a routing policy remains stable, where stability for a routing policy is achieved if the number of outstanding requests is uniformly bounded over time. To obtain this characterization, we first establish a threshold on the proportion of adversarial agents above which previously stable routing policies for fully cooperative fleets are provably unstable. We then derive a sufficient condition on the fleet size to recover stability given a maximum proportion of adversarial agents. We empirically validate our theoretical results on a case study on autonomous taxi routing, where we consider transportation requests from real San Francisco taxicab data.

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Roee M. Francos, Daniel Garces, Stephanie Gil. 2025-04-01. Provably Stable Multi-Agent Routing with Bounded-Delay Adversaries in the Decision Loop. https://arxiv.org/abs/2504.00863

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