Markus Iser, Jakob Bach

Project Description

Supervised and unsupervised learning can be used to exploit the empirically observable complementarity of algorithms for hard combinatorial problems (SAT) which is not well understood yet from an analytical perspective. Explainable machine learning can also provide meaningful insights about the method under evaluation and can moreover help to reduce the number of runtime experiments for evaluating new SAT algorithms. The provided thesis is under co-supervision of Jakob Bach (Data Science, IPD Böhm) and Markus Iser (SAT Algorithms, ITI Sanders).