Experiments with Strategy Learning for E Prover

Automated theorem provers (ATPs) consist of a number of complicated algorithms, that can be parameterized and combined together in different ways. Examples of such parameterizations are clause weighting and selection schemes, term orderings, sets of inference and reduction rules used, etc. E [8] (as some other ATPs) has a language for packaging such useful combinations of parameterizations into strategies. Over 200 strategies have been named and are part of the E source code. Such a large number of strategies can be used to experiment with data-driven methods that try to estimate how to solve a new problem, by considering a large database of previously solved problems and their suitable characterization. E is probably the first ATP that has applied machine learning to strategy selection. There are different ways how to do this, and how to optimize a large set of strategies in general. We will consider some of them and report some results obtained.