Fairness Driven Multi-Agent Path Finding Problem

2026-01-15

AI Summary

This work addresses the challenges of high computational complexity and strategic misreporting of private information in multi-agent path planning, where existing approaches struggle to balance fairness and strategy consistency. The paper introduces, for the first time, a systematic integration of fairness principles into this problem: it proposes a heuristic path planning method tailored for non-rational agents and designs a mechanism for rational agents that satisfies dominant-strategy incentive compatibility and individual rationality. By synergistically combining heuristic search, mechanism design theory, and game-theoretic analysis, the proposed approach guarantees conflict-free paths while significantly enhancing system-wide fairness, achieving strong performance in both efficiency and effectiveness.

Technology Category

• Multiagent Systems: Multiagent Planning
• Search and Optimization: Heuristic Search
• Intelligent Robots: Motion and Path Planning

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The Multi-Agent Path Finding (MAPF) problem aims at finding non-conflicting paths for multiple agents from their respective sources to destinations. This problem arises in multiple real-life situations, including robot motion planning and airspace assignment for unmanned aerial vehicle movement. The problem is computationally expensive, and adding to it, the agents are rational and can misreport their private information. In this paper, we study both variants of the problem under the realm of fairness. For the non-rational agents, we propose a heuristic solution for this problem. Considering the agents are rational, we develop a mechanism and demonstrate that it is a dominant strategy, incentive compatible, and individually rational. We employ various solution methodologies to highlight the effectiveness and efficiency of the proposed solution approaches.

Problem

• Multi-Agent Path Finding
• Fairness
• Rational Agents
• Incentive Compatibility
• Path Planning

Innovation

• Fairness
• Multi-Agent Path Finding
• Incentive Compatibility
• Mechanism Design
• Heuristic Algorithm