merging example plans into generalized plans for non-deterministic environments
We present a new approach for finding generalized contingent plans with loops and branches in situations where there is uncertainty in state properties and object quantities, but lack of probabilistic information about these uncertainties. We use a state abstraction technique from static analysis of programs, which uses 3-valued logic to compactly represent belief states with unbounded numbers of objects. Our approach for finding plans is to incrementally generalize and merge input example plans which can be generated by classical planners. The expressiveness and scope of this approach are demonstrated using experimental results on common benchmark domains.
planning