AINeutralarXiv – CS AI · 8h ago6/10
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Learning Admissible Heuristics via Cost Partitioning
Researchers have developed a machine-learning framework that learns to create admissible heuristics for optimal planning by leveraging cost partitioning and Lagrangian duality. The approach uses graph neural networks with Weisfeiler-Leman algorithms to generate cost weights that guarantee admissibility by construction, marking the first learned heuristic with formal optimality guarantees.