The algorithms employed by our communities are often underspecified, and thus have multiple implementation choices, which do not effect the correctness of the output, but do impact the efficiency or even tractability of its production. In this extended abstract, to accompany a keynote talk at the 2021 SC-Square Workshop, we survey recent work (both the author's and from the literature) on the use of Machine Learning technology to improve algorithms of interest to SC-Square.

翻译：我们社区采用的算法往往没有详细说明,因此有多种执行选择,这些选择并不影响产出的正确性,但确实影响其生产的效率甚至可移动性。 在这份广泛的抽象文章中,在2021年SC-Square研讨会上,我们结合主旨演讲,调查了最近关于利用机器学习技术改进SC-Square感兴趣的算法的工作(包括作者和文献)。