基于条件重写规则的公式化简器自动生成
Automatic Generation of Formula Simplifiers based on Conditional Rewrite Rules
摘要：
本文研究了基于条件项重写的逻辑公式增广器生成问题。特别是，本文重点介绍了一个程序合成的应用程序，公式简化已被证明有显着的影响。我们表明，通过将机器学习技术与基于约束的合成相结合，可以从代表性问题的语料库中完全自动地合成公式简化器，从而可以创建针对特定问题域的公式简化器。我们展示了我们的方法的好处，从SyGuS竞争和自动评分的综合基准。
英文摘要：
This paper addresses the problem of creating simplifiers for logic formulas based on conditional term rewriting. In particular, the paper focuses on a program synthesis application where formula simplifications have been shown to have a significant impact. We show that by combining machine learning techniques with constraint-based synthesis, it is possible to synthesize a formula simplifier fully automatically from a corpus of representative problems, making it possible to create formula simplifiers tailored to specific problem domains. We demonstrate the benefits of our approach for synthesis benchmarks from the SyGuS competition and automated grading.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
