Two new research papers introduce novel methods for learning admissible heuristics in AI planning and combinatorial search. One paper proposes a framework that learns cost partitions using deep learning and graph algorithms, guaranteeing heuristic admissibility. The other paper presents a method for training neural heuristics with an underestimating operator and a post-hoc calibration to ensure they never overestimate costs, preserving solution optimality in search algorithms. AI
IMPACT These methods could significantly improve the efficiency and optimality of AI search algorithms in complex problem-solving scenarios.
RANK_REASON Two academic papers published on arXiv detailing new methods for learning admissible heuristics in AI.
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