Automated nogood-filtered fine-grained streamlining: a case study on covering arrays

We present an automated method to enhance constraint models through fine-grained streamlining, leveraging no good information from learning solvers. This approach reformulates the streamlining process by filtering streamliners based on nogood data from the SAT solver CaDiCaL. Our method generates candidate streamliners from high-level Essence specifications, constructs a streamliner portfolio using Monte Carlo Tree Search, and applies these to unseen problem instances. The key innovation lies in utilising learnt clauses to guide streamliner filtering, effectively reformulating the original model to focus on areas of high search activity. We demonstrate our approach on the Covering Array Problem, achieving significant speedup compared to the state-of-the-art coarse-grained method. This work not only enhances solver efficiency but also provides new insights into automated model reformulation, with potential applications across a wide range of constraint satisfaction problems.