  In order to classify the indeterministic termination behavior of the open
source SAT solver CryptoMiniSat in multi-threading mode while processing hard
to solve boolean satisfiability problem instances, internal solver runtime
parameters have been collected and analyzed. A subset of these parameters has
been selected and employed as features vector to successfully create a machine
learning model for the binary classification of the solver's termination
behavior with any single new solving run of a not yet solved instance. The
model can be used for the early estimation of a solving attempt as belonging or
not belonging to the class of candidates with good chances for a fast
termination. In this context a combination of active profiles of runtime
characteristics appear to mirror the influence of the solver's momentary
heuristics on the immediate quality of the solver's resolution process. Because
runtime parameters of already the first two solving iterations are enough to
forecast termination of the attempt with good success scores, the results of
the present work deliver a promising basis which can be further developed in
order to enrich CryptoMiniSat or generally any modern SAT solver with AI
abilities.
