The problem of planning in the presence of sensing has been addressed in recent years as
a non-deterministic search problem in belief space. In this work, we use ideas advanced
recently for compiling conformant problems into classical problems, for introducing a different approach where contingent problems P are mapped into non-deterministic problems
X(P) in state space. We also identify a contingent width parameter, and show that for
problems P with bounded contingent width,
the translation is sound, polynomial, and complete.
We then solve X(P) by using a relaxation X+(P) that is a classical planning problem.
The formulation is tested experimentally
over contingent benchmarks where it is shown
to yield a planner that scales up better than existing contingent planners.