一种基于神经网络的DPLL SAT求解决策和搜索启发式算法

Raihan H. Kibria
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引用次数: 3

摘要

布尔可满足性问题的求解是许多应用的重要基础技术。工业应用中最有效的SAT求解器是基于带有子句学习和冲突分析依赖决策启发式的DPLL算法。对求解器MINISAT V1.14进行了修改,使用基于神经网络的决策启发式和搜索策略。多层前馈神经网络的权值和偏差由一种进化策略产生,该进化策略在SAT问题的样本集上进行训练。使用进化的解决方案解决的问题会遇到与原始程序相同数量的冲突,但需要更多的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolving a Neural Net-Based Decision and Search Heuristic for DPLL SAT Solvers
Solvers for the Boolean satisfiability problem are an important base technology for many applications. The most efficient SAT solvers for industrial applications are based on the DPLL algorithm with clause learning and conflict analysis dependent decision heuristics. The solver MINISAT V1.14 was modified to use a neural-net-based decision heuristic and search strategy. The weights and biases of the multilayer feedforward neural net are generated by an evolution strategy which is trained on a sample set of SAT problems. Problems solved with the evolved solutions encounter a similar number of conflicts as the original program, but require a higher number of decisions.
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