  Constrained Horn Clauses (CHCs) are often used in automated program
verification. Thus, techniques for (dis-)proving satisfiability of CHCs are a
very active field of research. On the other hand, acceleration techniques for
computing formulas that characterize the N-fold closure of loops have
successfully been used for static program analysis. We show how to use
acceleration to avoid repeated derivations with recursive CHCs in resolution
proofs, which reduces the length of the proofs drastically. This idea gives
rise to a novel calculus for (dis)proving satisfiability of CHCs, called
Acceleration Driven Clause Learning (ADCL). We implemented this new calculus in
our tool LoAT and evaluate it empirically in comparison to other
state-of-the-art tools.
