Operator Mutexes and Symmetries for Simplifying Planning Tasks - Fišer, Daniel; Torralba, Álvaro; Shleyfman, Alexander (Proceedings of the AAAI Conference on Artificial Intelligence, 2019)
Simplifying classical planning tasks by removing operators while preserving at least one optimal solution can significantly enhance the performance of planners. In this paper, we introduce the notion of operator mutex, which is a set of operators that cannot all be part of the same (strongly) optimal plan. We propose four different methods for inference of operator mutexes and experimentally verify that they can be found in a sizable number of planning tasks. We show how operator mutexes can be used in combination with structural symmetries to safely remove operators from the planning task.