Automating Resolution is NP-Hard
Not on the map. Why?
Abstract (arXiv)
We show that the problem of finding a Resolution refutation that is at most polynomially longer than a shortest one is NP-hard. In the parlance of proof complexity, Resolution is not automatizable unless P = NP. Indeed, we show it is NP-hard to distinguish between formulas that have Resolution refutations of polynomial length and those that do not have subexponential length refutations. This also implies that Resolution is not automatizable in subexponential time or quasi-polynomial time unless NP is included in SUBEXP or QP, respectively.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Apr 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.
2 papers.
- 2024Intersymbolic AI: Interlinking Symbolic AI and Subsymbolic AI17 Jun 2024
- 2021Learning algorithms versus automatability of Frege systems20 Nov 2021