A Parametric Interpolation Framework for First-Order Theories
Paper in proceedings, 2013
Craig interpolation is successfully used in both hardware and software
model checking. Generating good interpolants, and hence automatic understanding of the quality of interpolants is however a very hard problem,
requiring non-trivial reasoning in first-order theories.
An important class of state-of-the-art interpolation algorithms
is based on recursive procedures that generate interpolants
from refutations of unsatisfiable conjunctions of formulas.
We analyze this type of algorithms and develop a theoretical framework,
called a parametric interpolation
framework, for arbitrary first-order theories and inference systems.
As interpolation-based verification approaches depend on the quality of interpolants,
our method can be used to derive
interpolants of different structure and strength, with
or without quantifiers, from the same proof.
We show that some well-known interpolation algorithms
are instantiations of our framework.