  In various scenarios, a single phase of modelling and solving is either not
sufficient or not feasible to solve the problem at hand. A standard approach to
solving AI planning problems, for example, is to incrementally extend the
planning horizon and solve the problem of trying to find a plan of a particular
length. Indeed, any optimization problem can be solved as a sequence of
decision problems in which the objective value is incrementally updated.
Another example is constraint dominance programming (CDP), in which search is
organized into a sequence of levels. The contribution of this work is to enable
a native interaction between SAT solvers and the automated modelling system
Savile Row to support efficient incremental modelling and solving. This allows
adding new decision variables, posting new constraints and removing existing
constraints (via assumptions) between incremental steps. Two additional
benefits of the native coupling of modelling and solving are the ability to
retain learned information between SAT solver calls and to enable SAT
assumptions, further improving flexibility and efficiency. Experiments on one
optimisation problem and five pattern mining tasks demonstrate that the native
interaction between the modelling system and SAT solver consistently improves
performance significantly.
