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arXiv AI By Luise Ge, Brendan Juba

Query-Driven Learning for Lifted Reasoning

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The paper introduces query-driven learning for lifted reasoning, aiming to combine learning and reasoning into a single process rather than treating them as separate stages. It extends previous work to the first-order setting and identifies a necessary and sufficient condition called bounded concealment for validating or refuting universal queries with partial data and a knowledge base. A quasi-polynomial-time algorithm is proposed, with soundness and completeness guarantees relative to space-bounded lifted proofs, and quasipolynomial sample and runtime bounds are shown to make lifted query-driven reasoning efficient even when unrestricted knowledge-base learning is not feasible.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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