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Hardness measures for gridworld benchmarks and performance analysis of real-time heuristic search algorithms

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Abstract

Gridworlds are one of the most popular settings used in benchmark problems for real-time heuristic search algorithms. However, no comprehensive studies have been published so far on how the difference in the density of randomly positioned obstacles affects the hardness of the problems. This paper presents two measures for characterizing the hardness of gridworld problems parameterized by obstacle ratio, and relates them to the performance of the algorithms. We empirically show that the peak locations of those measures and actual performance degradation of the basic algorithms (RTA* and LRTA*) almost coincide with each other for a wide variety of problem settings. Thus the measures uncover some interesting aspects of the gridworlds.

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References

  • Cheeseman, P., Kanefsky, B., Taylor, W.M.: Where the really hard problems are. In: Proceedings of the 12th International Joint Conference on Artificial Intelligence, pp. 331–337 (1991)

  • Crawford, J.M., Auton, L.D.: Experimental results on the crossover point in random 3-SAT. Artif. Intell. 81(2/3), 31–57 (1996)

    Article  MathSciNet  Google Scholar 

  • Furcy, D., Koenig, S.: Speeding up the convergence of real-time search. In: Proceedings of the 17th National Conference on Artificial Intelligence, pp. 891–897 (2000)

  • Hernández, C., Meseguer, P.: LRTA*(k). In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, pp. 1238–1243 (2005)

  • Hogg, T.: Exploiting problem structure as a search heuristic. International J. Mod. Phys. C 9, 13–29 (1998)

    Article  Google Scholar 

  • Hogg, T., Huberman, B.A., Williams, C.: Phase transitions and the search problem. Artif. Intell. 81(1), 1–15 (1996)

    Article  MathSciNet  Google Scholar 

  • Ishida, T.: Real-time bidirectional search: coordinated problem solving in uncertain situations. IEEE Trans. Pattern Anal. Mach. Intell. 18(6), 617–628 (1996)

    Article  Google Scholar 

  • Ishida, T., Korf, R.E.: Moving-target search. In: Proceedings of the 12th International Joint Conference on Artificial Intelligence, pp. 204–210 (1991)

  • Ishida, T., Korf, R.E.: Moving-target search: a real-time search for changing goals. IEEE Trans. Pattern Anal. Mach. Intell. 17(6), 609–619 (1995)

    Article  Google Scholar 

  • Knight, K.: Are many reactive agents better than a few deliberative ones? In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp 432–437 (1993)

  • Koenig, S., Likhachev, M.: D* Lite. In: Proceedings of the National Conference on Artificial Intelligence, pp. 476–483 (2002)

  • Korf, R.E.: Real-time heuristic search. Artif. Intell. 42(2/3), 189–211 (1990)

    Article  MATH  Google Scholar 

  • Krishnamachari, B., Wicker, S.B., Bejar, R., Pearlman, M.: Critical density thresholds in distributed wireless networks. In: Communications, Information and Network Security. Kluwer, Dordrecht (2002)

    Google Scholar 

  • Mitchell, D., Selman, B., Levesque, H.: Hard and easy distributions of SAT problems. In: Proceedings of the 10th National Conference on Artificial Intelligence, pp. 459–465 (1992)

  • Santi, P., Blough, D.M., Vainstein, F.: A probabilistic analysis for the range assignment problem in ad hoc networks. In: Proceedings of the 2nd ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 212–220 (2001)

  • Shimbo, M., Ishida, T.: Towards real-time search with inadmissible heuristics. In: Proceedings of the 14th European Conference on Artificial Intelligence, pp. 609–613 (2000)

  • Shimbo, M., Ishida, T.: Controlling the learning process of real-time heuristic search. Artif. Intell. 146(1), 1–41 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  • Silver, D.: Cooperative pathfinding. In: Proceedings of the Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 117–122 (2005)

  • Stentz, A.: The focussed D* algorithm for real-time replanning. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1652–1659 (1995)

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Correspondence to Masataka Mizusawa.

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Mizusawa, M., Kurihara, M. Hardness measures for gridworld benchmarks and performance analysis of real-time heuristic search algorithms. J Heuristics 16, 23–36 (2010). https://doi.org/10.1007/s10732-008-9084-0

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  • DOI: https://doi.org/10.1007/s10732-008-9084-0

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