We present Self-Driven Strategy Learning (sdsl), a lightweight online
learning methodology for automated reasoning tasks that involve solving a set
of related problems. sdsl automatically gathers information, in form of a
dataset, while solving earlier problems. It utilizes the learned data to adjust
the solving strategy for later problems by fitting a machine learning model to
the obtained data on the fly. We formally define the approach as a set of
abstract transition rules. We describe a concrete instance of the sdsl calculus
which uses conditional sampling for generating data and random forests as the
underlying machine learning model. We implement the approach on top of the
Kissat solver and show that the combination of Kissat+sdsl certifies larger
bounds and finds more counter-examples than other state-of-the-art bounded
model checking approaches on benchmarks obtained from the latest Hardware Model
Checking Competition.

自驱动策略学习是一种轻量级在线学习方法，适用于解决一组相关问题的自动化推理任务。它使用机器学习模型根据数据集调整后续问题的解决策略，从而实现更好的边界模型检查性能。