Sokoban: Evaluating standard single-agent search techniques in the presence of deadlock

  • Andreas Junghanns
  • Jonathan Schaeffer
Planning, Constraints, Search and Databases
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1418)


Single-agent search is a powerful tool for solving a variety of applications. Most of the academic application domains used to explore single-agent search techniques have the property that if you start with a solvable state, at no time in the search can you reach a state that is unsolvable (it may, however, not be minimal). In this paper we address the implications that arise when states in the search are unsolvable. These so-called deadlock states are largely responsible for the failure of our attempts to solve positions in the game of Sokoban.


single agent search heuristic search Sokoban deadlocks IDA 


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  1. 1.
    J. Culberson. Sokoban is PSPACE-complete. Technical Report TR 97-02, Dept. of Computing Science, University of Alberta, 1997. Also: Scholar
  2. 2.
    J. Culberson and J. Schaeffer. Searching with pattern databases. In G. McCalla, editor, Advances in Artificial Intelligence, pages 402–416. Springer-Verlag, 1996. Proceedings of CSCSI'95.Google Scholar
  3. 3.
    D. Dor and U. Zwick. SOKOBAN and other motion planing problems, 1995. At: Scholar
  4. 4.
    M. Ginsberg. Partition search. In Proceedings of the National Conference on Artificial Intelligence (AAAI-96), pages 228–233, 1996.Google Scholar
  5. 5.
    O. Hansson, A. Mayer, and M. Yung. Criticizing solutions to relaxed models yields powerful admissible heuristics. Information Sciences, 63(3):207–227, 1992.CrossRefGoogle Scholar
  6. 6.
    M Klein. A primal method for minimal cost flows. Management Science, 14:205–220, 1967.Google Scholar
  7. 7.
    R.E. Korf. Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence, 27(1):97–109, 1985.CrossRefMathSciNetGoogle Scholar
  8. 8.
    R.E. Korf. Macro-operators: A weak method for learning. Artificial Intelligence, 26(1):35–77, 1985.CrossRefGoogle Scholar
  9. 9.
    R.E. Korf. Real-time heuristic search. Artificial Intelligence, 42(2–3):189–211, 1990.CrossRefGoogle Scholar
  10. 10.
    R.E. Korf. Finding optimal solutions to Rubik's Cube using pattern databases. In AAAI National Conference, pages 700–705, 1997.Google Scholar
  11. 11.
    H.W. Kuhn. The Hungarian method for the assignment problem. Naval Res. Logist. Quart., pages 83–98, 1955.Google Scholar
  12. 12.
    A. Reinefeld and T.A. Marsland. Enhanced iterative-deepening search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(7):701–710, July 1994.CrossRefGoogle Scholar
  13. 13.
    D. Slate and L. Atkin. Chess 4.5 — The Northwestern University chess program. In P.W. Frey, editor, Chess Skill in Man and Machine, pages 82–118, New York, 1977. Springer-Verlag.Google Scholar
  14. 14.
    G. Wilfong. Motion planning in the presence of movable obstacles. In 4th ACM Symposium on Computational Geometry, pages 279–288, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Andreas Junghanns
    • 1
  • Jonathan Schaeffer
    • 1
  1. 1.Dept. of Computing ScienceUniversity of AlbertaEdmontonCanada

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