Weak, strong, and strong cyclic planning via symbolic model checking

Planning in nondeterministic domains yields both conceptual and practical difficulties. From the conceptual point of view, different notions of planning problems can be devised: for instance, a plan might either guarantee goal achievement, or just have some chances of success. From the practical point of view, the problem is to devise algorithms that can effectively deal with large state spaces. In this paper, we tackle planning in nondeterministic domains by addressing conceptual and practical problems. We formally characterize different planning problems, where solutions have a chance of success ("weak planning"), are guaranteed to achieve the goal ("strong planning"), or achieve; the goal with iterative trial-and-error strategies ("strong cyclic planning"). In strong cyclic planning, all the executions associated with the solution plan always have a possibility of terminating and, when they do, they are guaranteed to achieve the goal. We present planning algorithms for these problem classes, and prove that they are correct and complete. We implement the algorithms in the MBP planner by using symbolic model checking techniques. We show that our approach is practical with an extensive experimental evaluation: MBP compares positively with state-of-the-art planners, both in terms of expressiveness and in terms of performance.