A differentiable neural architecture learns lifted action schemas and identifies unobserved action arguments from state-change traces in planning domains.
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Differentiable Learning of Lifted Action Schemas for Classical Planning
A differentiable neural architecture learns lifted action schemas and identifies unobserved action arguments from state-change traces in planning domains.