Mihaela Sabin, Eugene C. Freuder
A wide variety of problems can be represented as constraint satisfaction problems (CSPs), and once so represented can be solved by a variety of effective algorithms. However, as with other powerful, general AI problem solving methods, we must still address the task of moving from a natural statement of the problem to a formulation of the problem as a CSP. This research addresses the task of automating this problem formulation process, using logic puzzles as a testbed. Beyond problem formulation per se, we address the issues of effective problem formulation, i.e. finding formulations that support more efficient solution, as well as incremental problem formulation that supports reasoning from partial information and are congenial to human thought processes.