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arXiv AI By Rajesh Mangannavar, Pranay Dugar, Prasad Tadepalli

GADAR: Generalizing Across Domains via Action Ranking for Classical Planning

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GADAR (Generalizing Across Domains via Action Ranking) is a learned policy for classical planning that accepts a domain description as input and constructs grounded actions in unseen domains without search. It uses a three‑layer architecture—lifted domain, binding, and chain layers—to embed domain structure, tie action applicability to lifted preconditions, and capture schema dependencies. In zero‑shot experiments across eight benchmark domains, a single GADAR model trained on seven domains solves 58.3% of test instances, matching closely the performance of domain‑specific models and outperforming random choice and a representation lacking the chain layer.

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