SAT Modulo Monotonic Theories

Sam Bayless, Noah Bayless, Holger Hoos, Alan Hu
2015 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Boolean satisfiability (SAT) solvers have been successfully applied to a wide variety of difficult combinatorial problems. Many further problems can be solved by SAT Modulo Theory (SMT) solvers, which extend SAT solvers to handle additional types of constraints. However, building efficient SMT solvers is often very difficult. In this paper, we define the concept of a Boolean monotonic theory and show how to easily build efficient SMT solvers, including effective theory propagation and clause
more » ... rning, for such theories. We present examples showing useful constraints that are monotonic, including many graph properties (e.g., shortest paths), and geometric properties (e.g., convex hulls). These constraints arise in problems that are otherwise difficult for SAT solvers to handle, such as procedural content generation. We have implemented several monotonic theory solvers using the techniques we present in this paper and applied these to content generation problems, demonstrating major speed-ups over SAT, SMT, and Answer Set Programming solvers, easily solving instances that were previously out of reach.
doi:10.1609/aaai.v29i1.9755 fatcat:ezcflwu74bhfxoczd6h53itp7q