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New MAGIC framework improves multi-agent pathfinding under map uncertainty

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework called MAGIC (Multi-Agent Gaussian belief Inference for Coordination) to address uncertainty in multi-agent pathfinding (MAPF). This system updates a shared belief about traversability in real-time based on agent observations, leveraging spatial correlations to infer the traversability of unobserved areas. Experiments show MAGIC significantly reduces execution costs compared to existing methods, proving effective even for large teams of up to 800 agents. AI

IMPACT Enhances robotic coordination and navigation in uncertain environments, potentially improving efficiency in logistics and autonomous systems.

RANK_REASON The cluster contains a research paper detailing a new algorithm for multi-agent pathfinding.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New MAGIC framework improves multi-agent pathfinding under map uncertainty

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 Deutsch(DE) · Viraj Parimi, Shao-Hung Chan, Han Zhang, Jingkai Chen, Brian Williams ·

    Belief-Aware Multi-Agent Path Finding under Map Uncertainty

    arXiv:2609.40269v1 Announce Type: new Abstract: Multi-Agent Path Finding (MAPF) aims to find collision-free paths for multiple agents in a shared environment. Classical MAPF assumes that all static obstacles are known in advance, but real-world environments can change unexpectedl…

  2. arXiv cs.MA (Multiagent) TIER_1 Deutsch(DE) · Brian Williams ·

    Belief-Aware Multi-Agent Path Finding under Map Uncertainty

    Multi-Agent Path Finding (MAPF) aims to find collision-free paths for multiple agents in a shared environment. Classical MAPF assumes that all static obstacles are known in advance, but real-world environments can change unexpectedly due to fallen objects, spills, or other local …

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