Automated Strategy Invention for Confluence of Term Rewrite Systems
This work addresses the complexity of parameter selection in automated term rewriting tools for software verification and compiler optimization, representing an incremental improvement over existing methods.
The paper tackles the problem of automating parameter selection for proving confluence in term rewrite systems by applying machine learning to invent strategies, resulting in a learning-guided prover that surpasses human-designed strategies on benchmark datasets and proves/disproves confluence for previously unsolved systems.
Term rewriting plays a crucial role in software verification and compiler optimization. With dozens of highly parameterizable techniques developed to prove various system properties, automatic term rewriting tools work in an extensive parameter space. This complexity exceeds human capacity for parameter selection, motivating an investigation into automated strategy invention. In this paper, we focus on confluence, an important property of term rewrite systems, and apply machine learning to develop the first learning-guided automatic confluence prover. Moreover, we randomly generate a large dataset to analyze confluence for term rewrite systems. Our results focus on improving the state-of-the-art automatic confluence prover CSI: When equipped with our invented strategies, it surpasses its human-designed strategies both on the augmented dataset and on the original human-created benchmark dataset Cops, proving/disproving the confluence of several term rewrite systems for which no automated proofs were known before.