Forecast-Aware Cooperative Planning on Temporal Graphs under Stochastic Adversarial Risk

2026-03-15

AI Summary

This work addresses the challenge of multi-robot coordination under dynamically evolving environmental risks—such as adversarial patrols—that exhibit stochastic yet temporally predictable patterns. Existing approaches often fail to exploit these temporal trends for effective planning. To overcome this limitation, the paper introduces a forecast-aware cooperative planning framework that models adversarial dynamics using a first-order Markov stay-move process, generating time-indexed edge-risk predictions. These forecasts are then integrated into a joint optimization of support node placement and path planning. By explicitly incorporating temporal risk prediction into the coordination mechanism, the proposed method enables proactive responses to dynamic stochastic threats, thereby transcending the restrictive assumption of static risk. Experimental results demonstrate that the framework substantially reduces the team’s expected total cost, achieving performance close to an ideal clairvoyant planner and significantly outperforming non-proactive baselines.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Cooperative multi-robot missions often require teams of robots to traverse environments where traversal risk evolves due to adversary patrols or shifting hazards with stochastic dynamics. While support coordination - where robots assist teammates in traversing risky regions - can significantly reduce mission costs, its effectiveness depends on the team's ability to anticipate future risk. Existing support-based frameworks assume static risk landscapes and therefore fail to account for predictable temporal trends in risk evolution. We propose a forecast-aware cooperative planning framework that integrates stochastic risk forecasting with anticipatory support allocation on temporal graphs. By modeling adversary dynamics as a first-order Markov stay-move process over graph edges, we propagate the resulting edge-occupancy probabilities forward in time to generate time-indexed edge-risk forecasts. These forecasts guide the proactive allocation of support positions to forecasted risky edges for effective support coordination, while also informing joint robot path planning. Experimental results demonstrate that our approach consistently reduces total expected team cost compared to non-anticipatory baselines, approaching the performance of an oracle planner.

Problem

• cooperative planning
• temporal graphs
• stochastic adversarial risk
• risk forecasting
• multi-robot coordination

Innovation

• forecast-aware planning
• stochastic risk forecasting
• cooperative multi-robot planning
• temporal graphs
• support coordination