Web: http://arxiv.org/abs/2107.09766

June 16, 2022, 1:11 a.m. | Minchao Wu, Takeshi Tsukada, Hiroshi Unno, Taro Sekiyama, Kohei Suenaga

cs.LG updates on arXiv.org arxiv.org

Loop-invariant synthesis is the basis of program verification. Due to the
undecidability of the problem in general, a tool for invariant synthesis
necessarily uses heuristics. Despite the common belief that the design of
heuristics is vital for the performance of a synthesizer, heuristics are often
engineered by their developers based on experience and intuition, sometimes in
an \emph{ad-hoc} manner. In this work, we propose an approach to systematically
learning heuristics for template-based CounterExample-Guided Inductive
Synthesis (CEGIS) with reinforcement learning. As …

ai arxiv heuristics learning reinforcement reinforcement learning template

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