Minh Binh Do and Subbarao Kambhampati
Although the deep affinity between Graphplan’s backward search, and the process of solving constraint satisfaction problems has been noted earlier, these relations have hither-to been primarily used to adapt CSP search techniques into the backward search phase of Graphplan. This paper describes GP-CSP, a system that does planning by automatically converting Graphplan’s planning graph into a CSP encoding, and solving the CSP encoding using standard CSP solvers. Our comprehensive empirical evaluation of GP-CSP demonstrates that it is quite competitive with both standard Graphplan and Blackbox system, which compiles planning graphs into SAT encodings. We discuss the many advantages offered by focusing on CSP encodings rather than SAT encodings, including the fact that by exploiting implicit constraint representations, GP-CSP tends to be less susceptible to memory blow-up associated with methods that compile planning problems into SAT encodings. Our work is inspired by the success of van Beek and Chen’s CPLAN system. However, in contrast to CPLAN, which expects hand-coded CSP encodings for individual domains and problems, GP-CSP is able to take domain descriptions in STRIPS (PDDL) representation, and automatically generate the CSP encodings.