Advertisement

A Survey and Classification of A* Based Best-First Heuristic Search Algorithms

  • Luis Henrique Oliveira Rios
  • Luiz Chaimowicz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6404)

Abstract

A* (a-star) is a well known best-first search algorithm that has been applied to the solution of different problems. In recent years, several extensions have been proposed to adapt it and improve its performance in different application scenarios. In this paper, we present a survey and classification of the main extensions to the A* algorithm that have been proposed in the literature. We organize them into five classes according to their objectives and characteristics: incremental, memory-concerned, parallel, anytime, and real-time. For each class, we discuss its main characteristics and applications and present the most representative algorithms.

Keywords

Path Planning Heuristic Search Multiagent System Digital Game 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100–107 (1968)CrossRefGoogle Scholar
  2. 2.
    Koenig, S., Likhachev, M.: Improved fast replanning for robot navigation in unknown terrain. In: Proc. of the Int. Conf. on Robotics and Automation, pp. 968–975 (2002)Google Scholar
  3. 3.
    Korf, R.E., Reid, M.: Complexity analysis of admissible heuristic search. In: Proceedings of the National Conference on Artificial Intelligence - AAAI (1998)Google Scholar
  4. 4.
    Koenig, S., Likhachev, M., Liu, Y., Furcy, D.: Incremental heuristic search in ai. AI Mag. 25(2), 99–112 (2004)Google Scholar
  5. 5.
    Sun, X., Yeoh, W., Koenig, S.: Dynamic fringe-saving a*. In: AAMAS 2009, pp. 891–898 (2009)Google Scholar
  6. 6.
    Koenig, S.: Dynamic fringe-saving a* (June 2010) , http://idm-lab.org/bib/abstracts/Koen09e.html (Retrieved July 2010)
  7. 7.
    Sun, X., Koenig, S.: The fringe-saving a* search algorithm - a feasibility study. In: IJCAI, pp. 2391–2397 (2007)Google Scholar
  8. 8.
    Sun, X., Koenig, S., Yeoh, W.: Generalized adaptive a*. In: Int. Foundation for Autonomous Agents and Multiagent Systems AAMAS 2008, pp. 469–476 (2008)Google Scholar
  9. 9.
    Koenig, S., Likhachev, M., Furcy, D.: Lifelong planning a*. Artif. Intell. 155(1-2), 93–146 (2004)CrossRefzbMATHGoogle Scholar
  10. 10.
    Stentz, A.: Optimal and efficient path planning for partially-known environments. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 3310–3317 (1994)Google Scholar
  11. 11.
    Ferguson, D., Stentz, A.: Field d*: An interpolation-based path planner and replanner. In: Proc. of the Int. Symposium on Robotics Research, pp. 1926–1931 (2005)Google Scholar
  12. 12.
    Zhou, R., Hansen, E.A.: Memory-bounded a* graph search. In: Proc. of the Fifteenth Int. Florida Artif. Intell. Research Society Conf., pp. 203–209. AAAI Press, Menlo Park (2002)Google Scholar
  13. 13.
    Korf, R.E.: Depth-first iterative-deepening: An optimal admissible tree search. Artif. Intell. 27, 97–109 (1985)CrossRefzbMATHGoogle Scholar
  14. 14.
    Korf, R.E.: Linear-space best-first search. Artif. Intell. 62(1), 41–78 (1993)CrossRefzbMATHGoogle Scholar
  15. 15.
    Russell, S.: Efficient memory-bounded search methods. In: ECAI 1992, pp. 1–5. Wiley, Chichester (1992)Google Scholar
  16. 16.
    Yoshizumi, T., Miura, T., Ishida, T.: A* with partial expansion for large branching factor problems. In: Proc. of the Seventeenth National Conf. on Artif. Intell. and Twelfth Conf. on Innovative Applications of Artif. Intell., pp. 923–929 (2000)Google Scholar
  17. 17.
    Korf, R.E.: Best-first frontier search with delayed duplicate detection. In: AAAI, pp. 650–657. AAAI Press, The MIT Press (2004)Google Scholar
  18. 18.
    Korf, R.E., Zhang, W., Thayer, I., Hohwald, H.: Frontier search. J. ACM 52(5), 715–748 (2005)CrossRefzbMATHGoogle Scholar
  19. 19.
    Evett, M., Hendler, J., Mahanti, A., Nau, D.: Pra*: massively parallel heuristic search. Technical report (1991)Google Scholar
  20. 20.
    Dutt, S., Mahapatra, N.R.: Parallel A* algorithms and their performance on hypercube multiprocessors. In: Proc. of the 7th Int. Parallel Processing Symposium, pp. 797–803. IEEE Computer Society Press, Los Alamitos (1993)Google Scholar
  21. 21.
    Burns, E., Lemons, S., Zhou, R., Ruml, W.: Best-first heuristic search for multi-core machines. In: IJCAI, pp. 449–455 (2009)Google Scholar
  22. 22.
    Teije, A., Harmelen, F.: Describing problem solving methods using anytime performance profiles. In: Proc. of the 14th European Conf. on Artif. Intell., pp. 181–185. IOS Press, Amsterdam (2000)Google Scholar
  23. 23.
    Hansen, E.A., Zhou, R.: Anytime heuristic search. J. Artif. Intell. Res (JAIR) 28, 267–297 (2007)zbMATHGoogle Scholar
  24. 24.
    Likhachev, M., Gordon, G., Thrun, S.: Ara*: Anytime a* with provable bounds on sub-optimality. In: NIPS 2003. MIT Press, Cambridge (2004)Google Scholar
  25. 25.
    Zhou, R., Hansen, E.A.: Multiple sequence alignment using anytime a*. In: Eighteenth National Conf. on Artif. Intell., pp. 975–976. AAAI, Menlo Park (2002)Google Scholar
  26. 26.
    Likhachev, M., Ferguson, D.I., Gordon, G.J., Stentz, A., Thrun, S.: Anytime dynamic a*: An anytime, replanning algorithm. In: ICAPS 2005, pp. 262–271. AAAI, Menlo Park (2005)Google Scholar
  27. 27.
    Koenig, S.: Agent-centered search. AI Mag. 22(4), 109–131 (2001)Google Scholar
  28. 28.
    Korf, R.E.: Real-time heuristic search. Artif. Intell. 42(2-3), 189–211 (1990)CrossRefzbMATHGoogle Scholar
  29. 29.
    Koenig, S., Likhachev, M.: Real-time adaptive a*. In: AAMAS 2006, pp. 281–288. ACM Press, New York (2006)Google Scholar
  30. 30.
    Ishida, T.: Real-time search for autonomous agents and multiagent systems. Autonomous Agents and Multi-Agent Systems 1(2), 139–167 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Luis Henrique Oliveira Rios
    • 1
  • Luiz Chaimowicz
    • 1
  1. 1.Departamento de Ciência da ComputaçãoUniversidade Federal de Minas GeraisBrazil

Personalised recommendations