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

Part of the Lecture Notes in Computer Science book series (LNAI,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

  • Goal State
  • Search Problem
  • Ideal Tree
  • Search Graph
  • Clockwise Order

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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References

  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. Korf, R.E.: Real-time heuristic search. Artificial Intelligence 42(2–3), 189–211 (1990)

    CrossRef  MATH  Google Scholar 

  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. 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. 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)

    MATH  Google Scholar 

  6. LaValle, S.M.: Planning algorithms. Cambridge University Press (2006)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. 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. 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. 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)

    CrossRef  Google Scholar 

  12. Hernández, C., Baier, J.A.: Avoiding and escaping depressions in real-time heuristic search. Journal of Artificial Intelligence Research 43, 523–570 (2012)

    MATH  MathSciNet  Google Scholar 

  13. Zelinsky, A.: A mobile robot exploration algorithm. IEEE Transactions on Robotics and Automation 8(6), 707–717 (1992)

    CrossRef  Google Scholar 

  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)

    CrossRef  MATH  MathSciNet  Google Scholar 

  15. Sturtevant, N.R.: Benchmarks for grid-based pathfinding. IEEE Transactions Computational Intelligence and AI in Games 4(2), 144–148 (2012)

    CrossRef  Google Scholar 

  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. 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 

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Correspondence to Nicolás Rivera .

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Rivera, N., Illanes, L., Baier, J.A. (2014). Real-Time Pathfinding in Unknown Terrain via Reconnection with an Ideal Tree. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-12027-0_6

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