Extending Clause Learning SAT Solvers with Complete Parity Reasoning

ICTAI '12 Proceedings of the 2012 IEEE 24th International Conference on Tools with Artificial Intelligence - Volume 01(2012)

引用 14|浏览0
暂无评分
摘要
Instances of logical cryptanalysis, circuit verification, and bounded model checking can often be succinctly represented as a combined satisfiability (SAT) problem where an instance is a combination of traditional clauses and parity constraints. This paper studies how such combined problems can be efficiently solved by augmenting a modern SAT solver with an xor-reasoning module in the DPLL(XOR) framework. A new xor-reasoning module that deduces all possible implied literals using incremental Gauss-Jordan elimination is presented. A decomposition technique that can greatly reduce the size of parity constraint matrices while still allowing to deduce all implied literals is presented. It is shown how to eliminate variables occuring only in parity constraints while preserving the decomposition. The proposed techniques are evaluated experimentally.
更多
查看译文
关键词
computability,constraint theory,inference mechanisms,learning (artificial intelligence),DPLL framework,SAT problem,bounded model checking,circuit verification,clause learning SAT solver,complete parity reasoning,decomposition technique,incremental Gauss-Jordan elimination,logical cryptanalysis,parity constraint matrices,satisfiability problem,xor-reasoning module,Boolean satisfiability,Gaussian elimination,parity constraints,parity reasoning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要