SC-Square: Future Progress with Machine Learning?
This is an incremental survey for researchers in symbolic computation and verification, focusing on applying existing ML methods to known bottlenecks.
The paper surveys recent work on using machine learning to improve algorithms in the SC-Square community, addressing underspecified algorithms that impact efficiency and tractability, but does not provide specific results or numbers.
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.