Dynamic Area Search with Shared Memory: A Meta-Framework to Improve Pathfinding Algorithms

Abstract

Finding the shortest path between two given objects/states is a common problem for many scenarios/applications. Although many algorithms have been proposed, most of them rely entirely on the heuristic metrics to guide the search for the optimal path. In this work, we proposed a novel and generic approach to learn the underlying structure of the environment while exploring the problem seamlessly. The approach, Dynamic Area Search with Shared Memory (DASSM), learns from already explored areas in the pathfinding problem and efficiently and dynamically reuse the information to guide the utilized pathfinding algorithms. We showed how DASSM can alleviate the computational overhead by limiting and focusing the search to regions that more likely have the optimal path based on the learned information. In addition, we elaborated on the implementation and technical details of the approach and revealed its feasibility to be implemented to a wide range of informed search algorithms. To test DASSM, we applied it for three common pathfinding algorithms and tested them on publicly available benchmarks. DASSM improved the performance in all cases and reduced the execution time up to 75%. Moreover, we examined adding random steps for DASSM, where the results revealed a potential improvement in the execution time.