A differentiable neural architecture learns lifted action schemas and identifies unobserved action arguments from state-change traces in planning domains.
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Algorithms and completeness results for learning equivalent STRIPS+ domains from traces under three partial-observability cases for states while assuming selected action arguments are fully observed.
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