Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding
#Multi‑Agent Path Finding #Lifelong MAPF #Guidance Graph Optimization #Mixed Guidance Graph #Edge directions #Edge weights #Soft and hard guidance #Quality Diversity #Neural network generation #Traffic patterns #Bidirectional graphs #Agent navigation
📌 Key Takeaways
- The study focuses on lifelong Multi‑Agent Path Finding (MAPF), where agents receive new goals after reaching their previous ones.
- Traditional GGO uses bidirectional weighted graphs, where weights merely discourage moves but do not forbid them.
- Authors propose Mixed Guidance Graph Optimization (MGGO) to provide strict guidance by optimizing both edge directions and weights.
- Two MGGO approaches are detailed: a two‑phase method that separately adjusts directions and weights, and a Quality Diversity (QD) framework that trains a neural network to generate optimal guidance.
- MGGO incorporates traffic‑pattern insights to produce direction‑aware guidance graphs, improving navigational efficiency.
📖 Full Retelling
On 26 February 2026, researchers Yulun Zhang, Varun Bhatt, Matthew C. Fontaine, Stefanos Nikolaidis, and Jiaoyang Li presented their latest work on advancing multi‑agent pathfinding for lifelong scenarios, titled *“Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi‑Agent Path Finding.”* The paper addresses a core limitation in current Guidance Graph Optimization (GGO) methods: while edge weights only soft‑penalize certain moves, agents still may traverse them. To enforce stricter navigation directives, the authors introduce Mixed Guidance Graph Optimization (MGGO), simultaneously refining edge directions and weights and incorporating traffic‑aware patterns to produce more reliable guidance for agents that continuously receive new goals.
🏷️ Themes
Multi‑agent systems, Artificial intelligence, Graph optimisation, Lifelong planning, Neural networks, Quality Diversity algorithms, Traffic modelling, Agent coordination
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Original Source
--> Computer Science > Multiagent Systems arXiv:2602.23468 [Submitted on 26 Feb 2026] Title: Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding Authors: Yulun Zhang , Varun Bhatt , Matthew C. Fontaine , Stefanos Nikolaidis , Jiaoyang Li View a PDF of the paper titled Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding, by Yulun Zhang and 4 other authors View PDF HTML Abstract: Multi-Agent Path Finding aims to move agents from their start to goal vertices on a graph. Lifelong MAPF continuously assigns new goals to agents as they complete current ones. To guide agents' movement in LMAPF, prior works have proposed Guidance Graph Optimization methods to optimize a guidance graph, which is a bidirected weighted graph whose directed edges represent moving and waiting actions with edge weights being action costs. Higher edge weights represent higher action costs. However, edge weights only provide soft guidance. An edge with a high weight only discourages agents from using it, instead of prohibiting agents from traversing it. In this paper, we explore the need to incorporate edge directions optimization into GGO, providing strict guidance. We generalize GGO to Mixed Guidance Graph Optimization , presenting two MGGO methods capable of optimizing both edge weights and directions. The first optimizes edge directions and edge weights in two phases separately. The second applies Quality Diversity algorithms to optimize a neural network capable of generating edge directions and weights. We also incorporate traffic patterns relevant to edge directions into a GGO method, making it capable of generating edge-direction-aware guidance graphs. Subjects: Multiagent Systems (cs.MA) ; Artificial Intelligence (cs.AI cs.RO) Cite as: arXiv:2602.23468 [cs.MA] (or arXiv:2602.23468v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2602.23468 Focus to learn more arXiv-issued D...
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