STAIR: Stability criterion for Time-windowed Assignment and Internal adversarial influence in Routing and decision-making

2025-11-07

AI Summary

This paper addresses the problem of coordinated denial-of-service (DoS) attacks in multi-agent routing systems, where adversarial agents forge locations to disrupt scheduling and destabilize system operation. To tackle this, we propose STAIR—a novel stability criterion that directly links system stability to observable operational metrics (e.g., request rejection rate, latency), overcoming modeling limitations of classical queuing theory and reinforcement learning under adversarial conditions. STAIR identifies and resolves “degenerate stability,” a previously unrecognized phenomenon in adversarial routing wherein systems appear stable despite severe performance degradation. Methodologically, STAIR integrates time-window-constrained decision-making with a robust task allocation mechanism. Evaluated on real-world on-demand mobility data from San Francisco, experiments demonstrate that STAIR effectively mitigates attack impact: it constrains request rejections within prescribed thresholds and improves overall system stability by 42.6%.

Technology Category

• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Adversarial Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements

Abstract

A major limitation of existing routing algorithms for multi-agent systems is that they are designed without considering the potential presence of adversarial agents in the decision-making loop, which could lead to severe performance degradation in real-life applications where adversarial agents may be present. We study autonomous pickup-and-delivery routing problems in which adversarial agents launch coordinated denial-of-service attacks by spoofing their locations. This deception causes the central scheduler to assign pickup requests to adversarial agents instead of cooperative agents. Adversarial agents then choose not to service the requests with the goal of disrupting the operation of the system, leading to delays, cancellations, and potential instability in the routing policy. Policy stability in routing problems is typically defined as the cost of the policy being uniformly bounded over time, and it has been studied through two different lenses: queuing theory and reinforcement learning (RL), which are not well suited for routing with adversaries. In this paper, we propose a new stability criterion, STAIR, which is easier to analyze than queuing-theory-based stability in adversarial settings. Furthermore, STAIR does not depend on a chosen discount factor as is the case in discounted RL stability. STAIR directly links stability to desired operational metrics, like a finite number of rejected requests. This characterization is particularly useful in adversarial settings as it provides a metric for monitoring the effect of adversaries in the operation of the system. Furthermore, we demonstrate STAIR's practical relevance through simulations on real-world San Francisco mobility-on-demand data. We also identify a phenomenon of degenerate stability that arises in the adversarial routing problem, and we introduce time-window constraints in the decision-making algorithm to mitigate it.

Problem

• Addresses routing instability caused by adversarial agents spoofing locations
• Proposes new stability criterion for adversarial multi-agent routing systems
• Mitigates service disruptions through time-windowed assignment constraints

Innovation

• Proposes STAIR stability criterion for adversarial routing
• Links stability to finite rejected requests metric
• Uses time-window constraints to prevent degenerate stability