Per-Map Algorithm Selection in Real-Time Heuristic Search

Real-time heuristic search is suitable for time-sensitive pathfinding and planning tasks when an AI-controlled nonplayable character must interleave its planning and plan execution. Since its inception in the early 90s, numerous realtime heuristic search algorithms have been proposed. Many of the algorithms also have control parameters leaving a practitioner with a bewildering array of choices. Recent work treated the task of algorithm and parameter selection as a search problem in itself. Such automatically found algorithms outperformed previously known manually designed algorithms on the standard video-game pathfinding benchmarks. In this paper we follow up by selecting an algorithm and parameters automatically per map. Our sampling-based approach is efficient on the standard video-game pathfinding benchmarks. We also apply the approach to per-problem algorithm selection and while it is effective there as well, it is not practical. We offer suggestions on making it so.