The Road to Satisfaction: An Introduction to Constraint Programming

There are many combinatorial problems in artificial intelligence, computer hardware design, production scheduling, timetabling, and product configuration that can be formulated and solved using boolean satisfiability (SAT) or constraint programming (CP). Over the past 10-15 years significant advances have been made in terms of the scalability of these problem solving approaches to the point where we can now solve instances with millions of variables. The'no free lunch' theorem tells us that there is no one best method for solving combinatorial problems. This opens the door for the application of machine learning techniques to improve the use of SAT and CP methods.