Real-Time Pathfinding in Unknown Terrain via Reconnection with an Ideal Tree

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8864)

Abstract

In real-time pathfinding in unknown terrain an agent is required to solve a pathfinding problem by alternating a time-bounded deliberation phase with an action execution phase. Real-time heuristic search algorithms are designed for general search applications with time constraints but unfortunately in pathfinding they are known to produce poor-quality solutions. In this paper we propose \(\mathrm{p-FRIT }_\mathrm{{RT}}\), a real-time version of FRIT, a recently proposed algorithm able to produce very good-quality solutions in pathfinding under strict, but not fully real-time constraints. The idea underlying \(\mathrm{p-FRIT }_\mathrm{{RT}}\) draws inspiration from bug algorithms, a family of pathfinding algorithms. Yet, as we show, \(\mathrm{p-FRIT }_\mathrm{{RT}}\) is able to outperform a well-known bug algorithm and is able to solve graph search problems that are more general than pathfinding. \(\mathrm{p-FRIT }_\mathrm{{RT}}\) also outperforms significantly—generating solutions six times shorter when time constraints are tight—a previously proposed real-time version of FRIT and the real-time heuristic search algorithm that is considered to have state-of-the-art performance in real-time pathfinding.

Keywords

Depression 

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References

  1. 1.
    Bulitko, V., Björnsson, Y., Sturtevant, N., Lawrence, R.: Applied Research in Artificial Intelligence for Computer Games. In: Real-time Heuristic Search for Game Pathfinding, pp. 1–30. Springer (2011)Google Scholar
  2. 2.
    Korf, R.E.: Real-time heuristic search. Artificial Intelligence 42(2–3), 189–211 (1990)CrossRefMATHGoogle Scholar
  3. 3.
    Ishida, T.: Moving target search with intelligence. In: Proc. of the 10th National Conf. on Artificial Intelligence (AAAI), pp. 525–532 (1992)Google Scholar
  4. 4.
    Rivera, N., Illanes, L., Baier, J.A., Hernández, C.: Reconnecting with the ideal tree: An alternative to heuristic learning in real-time search. In: Proc. of the 6th Symposium on Combinatorial Search (SoCS) (2013)Google Scholar
  5. 5.
    Rivera, N., Illanes, L., Baier, J.A., Hernández, C.: Reconnection with the ideal tree: A new approach to real-time search. Journal of Artificial Intelligence Research 50, 235–264 (2014)MATHGoogle Scholar
  6. 6.
    LaValle, S.M.: Planning algorithms. Cambridge University Press (2006)Google Scholar
  7. 7.
    Hart, P.E., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimal cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100–107 (1968)CrossRefGoogle Scholar
  8. 8.
    Koenig, S., Likhachev, M.: Real-time Adaptive A*. In: Proc. of the 5th Int’l Joint Conf. on Autonomous Agents and Multi Agent Systems (AAMAS), pp. 281–288 (2006)Google Scholar
  9. 9.
    Hernández, C., Meseguer, P.: LRTA*(\(k\)). In: Proc. of the 19th int’l Joint Conf. on Artificial Intelligence (IJCAI), pp. 1238–1243 (2005)Google Scholar
  10. 10.
    Hernández, C., Meseguer, P.: Improving LRTA*(\(k\)). In: Proc. of the 20th Int’l Joint Conf. on Artificial Intelligence (IJCAI), pp. 2312–2317 (2007)Google Scholar
  11. 11.
    Koenig, S., Sun, X.: Comparing real-time and incremental heuristic search for real-time situated agents. Autonomous Agents and Muti-Agent Systems 18(3), 313–341 (2009)CrossRefGoogle Scholar
  12. 12.
    Hernández, C., Baier, J.A.: Avoiding and escaping depressions in real-time heuristic search. Journal of Artificial Intelligence Research 43, 523–570 (2012)MATHMathSciNetGoogle Scholar
  13. 13.
    Zelinsky, A.: A mobile robot exploration algorithm. IEEE Transactions on Robotics and Automation 8(6), 707–717 (1992)CrossRefGoogle Scholar
  14. 14.
    Lumelsky, V.J., Stepanov, A.A.: Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape. Algorithmica 2, 403–430 (1987)CrossRefMATHMathSciNetGoogle Scholar
  15. 15.
    Sturtevant, N.R.: Benchmarks for grid-based pathfinding. IEEE Transactions Computational Intelligence and AI in Games 4(2), 144–148 (2012)CrossRefGoogle Scholar
  16. 16.
    Harabor, D.D., Grastien, A.: Online graph pruning for pathfinding on grid maps. In: Proc. of the 26th AAAI Conf. onArtificial Intelligence (AAAI) (2011)Google Scholar
  17. 17.
    Uras, T., Koenig, S., Hernández, C.: Subgoal graphs for optimal pathfinding in eight-neighbor grids. In: Proc. of the 23rd Int’l Conf. on Automated Planning and Scheduling (ICAPS) (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nicolás Rivera
    • 1
  • León Illanes
    • 2
  • Jorge A. Baier
    • 2
  1. 1.Department of InformaticsKing’s College LondonLondonUK
  2. 2.Departmento de Ciencia de la ComputaciónPontificia Universidad Católica de ChileSantiagoChile

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