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This paper addresses the problem of creating simplifiers for logic formulasbased on conditional term rewriting. In particular, the paper focuses on aprogram synthesis application where formula simplifications have been shown tohave a significant impact. We show that by combining machine learningtechniques with constraint-based synthesis, it is possible to synthesize aformula simplifier fully automatically from a corpus of representativeproblems, making it possible to create formula simplifiers tailored to specificproblem domains. We demonstrate the benefits of our approach for synthesisbenchmarks from the SyGuS competition and automated grading.

Automatic Generation of Formula Simplifiers based on Conditional Rewrite Rules