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Real-Time Heuristic Search for Pathfinding in Video Games

  • Vadim Bulitko
  • Yngvi Björnsson
  • Nathan R. Sturtevant
  • Ramon Lawrence
Chapter

Abstract

Game pathfinding is a challenging problem due to a limited amount of per-frame CPU time commonly shared among many simultaneously pathfinding agents. The challenge is rising with each new generation of games due to progressively larger and more complex environments and larger numbers of agents pathfinding in them. Algorithms based on A* tend to scale poorly as they must compute a complete, possibly abstract, path for each agent before the agent can move. Real-time heuristic search algorithms satisfy a constant bound on the amount of planning per move, independent of problem size. These algorithms are thus a promising approach to large scale multi-agent pathfinding in video games. However, until recently, real-time heuristic search algorithms universally exhibited a visually unappealing “scrubbing” behavior by repeatedly revisiting map locations. This had prevented their adoption by video game developers. In this chapter, we review three modern search algorithms which address the “scrubbing” problem in different ways. Each algorithm presentation is complete with an empirical evaluation on game maps.

Keywords

Video Game Optimal Path Goal State Heuristic Function Open List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgements

Parts of this chapter have been previously published as conference and journal articles written by the authors. This research was supported by grants from the National Science and Engineering Research Council of Canada (NSERC) and the Icelandic Centre for Research (RANNÍS). All the research works by Nathan Sturtevant included in this chapter were performed at the University of Alberta. We appreciate the help of Josh Sterling, Stephen Hladky, and Daniel Huntley.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Vadim Bulitko
    • 1
  • Yngvi Björnsson
  • Nathan R. Sturtevant
  • Ramon Lawrence
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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