Efficient Incremental Modelling and Solving

TL;DR

This paper introduces a native interaction framework between SAT solvers and the Savile Row modelling system, enabling efficient incremental problem solving by adding/removing constraints and variables while retaining learned information.

Contribution

It presents a novel integration that allows incremental modelling and solving with SAT solvers, improving flexibility and performance in optimization and pattern mining tasks.

Findings

Significant performance improvements in experiments

Effective retention of learned information between solver calls

Enhanced flexibility through support for assumptions

Abstract

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.