Mining Backbone Literals in Incremental SAT
In incremental SAT solving, information gained from previous similar instances has so far been limited to learned clauses that are still relevant, and heuristic information such as activity weights and scores. In most settings in which incremental satisfiability is applied, many of the instances along the sequence of formulas being solved are unsatisfiable. We show that in such cases, with a P-time analysis of the proof, we can compute a set of literals that are logically implied by the next instance. By adding those literals as assumptions, we accelerate the search.
KeywordsModel Check Bounded Model Check Empty Clause Resolution Graph Resolution Proof
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- 10.Kroening, D., Strichman, O.: Decision procedures - an algorithmic point of view. Theoretical Computer Science. Springer-Verlag, February 2008 (to be published)Google Scholar
- 12.Nadel, A.: Understanding and Improving a Modern SAT Solver. PhD thesis, Tel Aviv University, Tel Aviv, Israel, August 2009Google Scholar
- 13.Nadel, A.: Boosting minimal unsatisfiable core extraction. In: Bloem, R., Sharygina, N. (eds.) FMCAD (2010)Google Scholar
- 14.Nadel, A., Ryvchin, V., Strichman, O.: Efficient MUS extraction with resolution. In: FMCAD, pp. 197–200. IEEE (2013)Google Scholar
- 16.Shtrichman, O.: Sharing information between instances of a propositional satisfiability (SAT) problem, December 2000. US provisional patent (60/257,384). Later became patent US2002/0123867 A1Google Scholar
- 18.Whittemore, J., Kim, J., Sakallah, K.: Satire: a new incremental satisfiability engine. In: IEEE/ACM Design Automation Conference (DAC) (2001)Google Scholar