#quality-diversity2 articles

2 articles

AINeutralarXiv – CS AI · 4h ago1

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QD-MAPPER: A Quality Diversity Framework to Automatically Evaluate Multi-Agent Path Finding Algorithms in Diverse Maps

Researchers developed QD-MAPPER, a framework using Quality Diversity algorithms and Neural Cellular Automata to automatically generate diverse maps for evaluating Multi-Agent Path Finding (MAPF) algorithms. This addresses the limitation of testing MAPF algorithms on fixed, human-designed maps that may not cover all scenarios and could lead to overfitting.

AINeutralarXiv – CS AI · 4h ago1

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Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding

Researchers propose Mixed Guidance Graph Optimization (MGGO) to improve multi-agent pathfinding systems by optimizing both edge directions and weights in guidance graphs. The paper introduces two MGGO methods, including one using Quality Diversity algorithms with neural networks, to provide stricter guidance for agent movement in lifelong scenarios.