Sokoban Explained

is a puzzle video game in which the player pushes boxes around in a warehouse, trying to get them to storage locations. The game was designed in 1981 by Hiroyuki Imabayashi, and first published in December 1982.

Gameplay

The warehouse is depicted as a grid of squares, each one representing either a floor section or a wall section. Some floor squares contain boxes and some are marked as storage locations. The player, often represented as a worker character, can move one square at a time horizontally or vertically onto empty floor squares, but cannot pass through walls or boxes.

To move a box, the player walks up to it and pushes it to an empty square directly beyond the box. Boxes cannot be pushed to squares with walls or other boxes, and they cannot be pulled. The number of boxes matches the number of storage locations. The puzzle is solved when all boxes occupy the storage locations.

Challenges and strategy

Progressing through the game often requires meticulous planning and strategic maneuvering. A single misstep, like pushing a box into a corner or blocking others, can create unsolvable scenarios, forcing the player to backtrack or restart the puzzle. Anticipating the consequences of each push, and considering the overall layout of the puzzle are crucial to avoid these deadlocks.[1]

Development

Sokoban was created in 1981 by Hiroyuki Imabayashi.[2] [3] The first commercial game was published in December 1982 by Thinking Rabbit, a software house based in Takarazuka, Japan. Sokoban was a hit in Japan, selling more than 400,000 copies before being released in the United States.[4] In 1988, Sokoban was published in US by Spectrum HoloByte for the IBM PC, Commodore 64, and Apple II as Soko-Ban.[5]

Implementations

Sokoban has been implemented for almost all home computers, personal computers, video game consoles and even some TVs.[6] Versions also exist for mobile phones, graphing calculators, digital cameras[7] and electronic organizers.

Scientific research

Sokoban has been studied using the theory of computational complexity. The computational problem of solving Sokoban puzzles was first shown to be NP-hard.[8] [9] Further work proved it is also PSPACE-complete.[10] [11]

Solving non-trivial Sokoban puzzles is difficult for computers because of the high branching factor (many legal pushes at each turn) and the large search depth (many pushes needed to reach a solution).[12] [13] Even small puzzles can require lengthy solutions.[14]

The Sokoban game provides a challenging testbed for developing and evaluating planning techniques. [15] [16] The first documented automated solver was Rolling Stone, developed at the University of Alberta. Its core principles laid the groundwork for many newer solvers. It employed a conventional search algorithm enhanced with domain-specific knowledge.[17] Festival, utilizing its FESS algorithm, was the first automatic solver to complete all 90 puzzles in the widely used XSokoban test suite.[18] [19] However, even the best automated solvers cannot solve many of the more challenging puzzles that humans can solve with time and effort.[20] [21]

Variants

Several puzzles can be considered variants of the original Sokoban game in the sense that they all make use of a controllable character pushing boxes around in a maze.

Selected official releases

This table lists some prominent official Sokoban releases that mark milestones, such as expanding to new platforms or achieving widespread popularity. They are organized by release date.

YearTitleCountryPlatformPublisherMedia
1982JapanNEC PC-8801Thinking RabbitCassette tape
1983JapanNEC PC-8801PCマガジンType-in program
1984JapanNEC PC-8801Thinking RabbitCassette tape
1986JapanFamicomASCIIFloppy
1988Soko-BanUSIBM PC, XT, and ATSpectrum HoloByteFloppy
1989JapanNEC PC-9801Thinking RabbitFloppy
1990BoxyboyUSTurboGrafx-16NECHuCard
1990Shove It! ...The Warehouse GameUSSega GenesisDreamWorksROM cartridge
1991JapanNEC PC-9801Thinking RabbitFloppy
2016Japan, USAndroid and Apple iOSThinking RabbitDigital distribution
2018JapanWindowsThinking RabbitDigital distribution
2019JapanNintendo Switch and PlayStation 4UnbalanceDigital distribution
2021The SokobanUSNintendo Switch and PlayStation 4UnbalanceDigital distribution

See also

External links

Notes and References

  1. Jean-Noël Demaret . François Van Lishout . Pascal Gribomont . Hierarchical Planning and Learning for Automatic Solving of Sokoban Problems . 2008 . 1,2,5.
  2. Web site: Thinking Rabbit - 1983 Developer Interview .
  3. Web site: My conversation with Mr Hiroyuki Imabayashi .
  4. Lafe Low . . News Line; Made in Japan . November 1988 . 14.
  5. Austin Barr . Calvin Chung . Aaron Williams . Block Dude Puzzles are NP-Hard (and the Rugs Really Tie the Reductions Together) . 1 . 2021 . CCCG (2021) .
  6. Web site: I Review the Game Built into My New CRT (Boxman). . 22 November 2020 .
  7. Web site: CHDK 1.5 User Manual . 2023-07-13 . CHDK Wiki . en.
  8. Michael Fryers . Michael Greene . Sokoban . Eureka . 54 . 1995 . 25–32 .
  9. Dorit Dor . Dorit Dor . Uri Zwick . Uri Zwick . SOKOBAN and other motion planning problems . . 13 . 4 . 1999 . 215–228 . 10.1016/S0925-7721(99)00017-6 . free.
  10. Joseph C. Culberson . Sokoban is PSPACE-complete . Technical Report TR 97-02, Dept. Of Computing Science, University of Alberta . 1997 .
  11. Robert Aubrey Hearn . Games, Puzzles, and Computation . PhD . 98–100 . Massachusetts Institute of Technology . 2006 .
  12. Andreas Junghanns . Jonathan Schaeffer . Sokoban: Improving the Search with Relevance Cuts . Theoretical Computer Science . 2001 . 252 . 1–2 . 5 . 10.1016/S0304-3975(00)00080-3 . free.
  13. Web site: Yaron Shoham . FESS Draft . 3 . 2020 .
  14. Web site: David Holland . Yaron Shoham . Theoretical analysis on Picokosmos 17 . https://web.archive.org/web/20160607071224/http://www.abelmartin.com/rj/sokobanJS/sokoban-jd.blogspot/sokoban_lessons/picokosmos17/analysis.htm . 2016-06-07.
  15. Andreas Junghanns . Jonathan Schaeffer . Sokoban: Evaluating standard single-agent search techniques in the presence of deadlock . 1998 . 4.
  16. Timo Virkkala . Solving Sokoban . MSc . 1 . University of Helsinki . 2011 .
  17. Andreas Junghanns . Jonathan Schaeffer . Sokoban: Enhancing general single-agent search methods using domain knowledge . Artificial Intelligence . 129 . 1–2 . 2001 . 219–251 . 10.1016/S0004-3702(01)00109-6 . free.
  18. Yaron Shoham . Jonathan Shaeffer . The FESS Algorithm: A Feature Based Approach to Single-Agent Search . 2020 . 2020 IEEE Conference on Games (CoG) . Osaka, Japan . 10.1109/CoG47356.2020.9231929 . IEEE.
  19. Web site: Yaron Shoham . FESS presentation at the CoG conference (17.5 minutes) . archive.org . en . video . 2020.
  20. Web site: Let's Logic Bots Statistics . 19 April 2024.
  21. Web site: Sokoban Solver Statistics - Large Test Suite . 14 April 2024.
  22. Web site: Frank Takes . Sokoban: Reversed Solving . 2008 .