Previous research in Artificial Intelligence has identified the possibility of simplifying planning problems via the identification and exploitation of symmetries. We advance the state of the art in algorithms that exploit symmetry in planning problems by generalizing previous approaches, and applying symmetry reductions to state-based planners. We suggest several algorithms for symmetry exploitation in state-based search, but also provide a comprehensive view through which additional algorithms can be developed and fine-tuned. We evaluate our approach to symmetry exploitation on instances from previous planning competitions, and demonstrate that our algorithms significantly improve the solution time of instances with symmetries.

In various domains, such as computer games, robotics, and transportation networks, shortest paths may need to be found quickly. Search time can be significantly reduced if it is known which parts of the graph include ``swamps''---areas that cannot lie on the only available shortest path, and can thus safely be pruned during search. We introduce an algorithm for detecting hierarchies of swamps, and exploiting them. Experiments support our claims of improved efficiency, showing significant reduction in search time.

In various domains, such as computer games, robotics, and transportation networks, shortest paths may need to be found quickly. Search time can be significantly reduced if it is known which parts of the graph include "swamps" - areas that cannot lie on the only available shortest path, and can thus safely be pruned during search. We introduce an algorithm for detecting hierarchies of swamps, and exploiting them. Experiments support our claims of improved efficiency, showing significant reduction in search time.