Per-Map Algorithm Selection in Real-Time Heuristic Search

Authors

  • Vadim Bulitko University of Alberta

DOI:

https://doi.org/10.1609/aiide.v12i1.12882

Keywords:

real-time heuristic search, video games, pathfinding, artificial intelligence

Abstract

Real-time heuristic search is suitable for time-sensitive pathfinding and planning tasks when an AI-controlled non-playable character must interleave its planning and plan execution. Since its inception in the early 90s, numerous real-time 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.

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Published

2021-06-25

How to Cite

Bulitko, V. (2021). Per-Map Algorithm Selection in Real-Time Heuristic Search. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 12(1), 143-148. https://doi.org/10.1609/aiide.v12i1.12882