Machine learning by satisfiability solving

Machine learning problems can be transformed into Boolean satisfiability (SAT) problems, allowing the use of SAT solvers for training. This approach involves representing the learning task as a set of Boolean equations and solving them to find a model that fits the data. While naive methods have high computational costs, advanced SAT solving techniques and approximation algorithms can make this approach more tractable for certain types of problems, especially those with a small number of logical parameters.