The player is confined to the board and may move horizontally or vertically onto empty squares (never through walls or boxes). The player can move a box by walking up to it and push it to the square beyond. Boxes cannot be pulled, and they cannot be pushed to squares with walls or other boxes. The number of boxes equals the number of storage locations. The puzzle is solved when all boxes are placed at storage locations.
|1982||Sokoban (倉庫番)||Japan||NEC PC-8801||Thinking Rabbit||Tape|
|1983||Sokoban [Extra Edition] (倉庫番[番外編])||Japan||NEC PC-8801||PCマガジン||Source code|
|1984||Sokoban 2 (倉庫番2)||Japan||NEC PC-8801||Thinking Rabbit||Tape|
|1988||Soko-Ban||US||IBM-PC and compatibles||Spectrum HoloByte||Floppy|
|1989||Soko-ban Perfect (倉庫番Perfect)||Japan||NEC PC-9801||Thinking Rabbit||Floppy|
|1990||Boxyboy||Japan, US||Turbografx-16 and PC Engine||NEC||HuCard|
|1991||Soko-ban Revenge (倉庫番Revenge)||Japan||NEC PC-9801||Thinking Rabbit||Floppy|
|2016||Sokoban Touch (倉庫番Touch)||Japan, US||Android and Apple iOS||Thinking Rabbit||Digital distribution|
In 1988 Sokoban was published in US by Spectrum HoloByte for the Commodore 64, IBM-PC and Apple II series as Soko-Ban. Sokoban was a hit in Japan, and had sold over 400,000 units in that country by the time Spectrum HoloByte imported it to the United States.
Implementations of Sokoban have been written for numerous computer platforms, including almost all home computer and personal computer systems. Different versions also exist for video game consoles, mobile phones, graphic calculators, digital cameras and electronic organizers.
Sokoban can be studied using the theory of computational complexity. The problem of solving Sokoban puzzles was first proved to be NP-hard. Further work showed that it was significantly more difficult than NP problems; it is PSPACE-complete. This is of interest for artificial intelligence (AI) research because solving Sokoban can be compared to the automated planning required by some autonomous robots.
Sokoban is difficult not only because of its large branching factor, but also because of its large search tree depth. Some level types can even be extended indefinitely, with each iteration requiring an exponentially growing number of moves and pushes. Skilled human players rely mostly on heuristics and are usually able to quickly discard a great many futile or redundant lines of play by recognizing patterns and subgoals, thereby drastically reducing the amount of search.
Some Sokoban puzzles can be solved automatically by using a single-agent search algorithm, such as IDA*; enhanced by several techniques that make use of domain-specific knowledge. This is the method used by Rolling Stone, a Sokoban solver developed by the University of Alberta GAMES Group. Festival was the first automatic solver to solve all 90 levels in the standard benchmark test suite. However, the more complex Sokoban levels are out of reach even for the best automated solvers.
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.
- Alternative tilings: In the standard game, the mazes are laid out on a square grid. Several variants apply the rules of Sokoban to mazes laid out on other tilings. Hexoban uses regular hexagons, and Trioban uses equilateral triangles.
- Multiple pushers: In the variants Multiban and Interlock, the player can control multiple characters.
- Alternative goals: Several variants adjust the requirements for completing a level. For example, in Block-o-Mania the boxes have different colours, and the goal is to push them onto squares with matching colours. Sokomind Plus implements a similar idea, with boxes and target squares uniquely numbered. In Interlock and Sokolor, the boxes also have different colours, but the goal is to move them so that similarly coloured boxes are adjacent. In CyberBox, each level has a designated exit square, and the goal is to reach that exit. In a variant called Beanstalk, the elements of the level must be pushed onto a target square in a fixed sequence.
- Additional game elements: Push Crate, Sokonex, Xsok, Cyberbox and Block-o-Mania all add new elements to the basic puzzle. Examples include holes, teleports, moving blocks and one-way passages. The 1982 Sokoban (NEC PC-8801) game featured levels with destructible walls.
- Character actions: In Pukoban, the character can pull boxes in addition to pushing them.
- Reverse mode: The player solves the puzzle backwards, from the end to the initial position by pulling instead of pushing boxes. Standard Sokoban puzzles can be played in reverse mode, and the reverse-mode solutions can be converted to solutions for the standard-mode puzzles. Therefore, reverse-mode gameplay can also be instrumental in solving standard Sokoban puzzles.
- "今回はこのゲームを開発した THINKING RABBIT さんにお願いして, 市販品とは別に10の倉庫をつくってもらいましたので" [This time, we asked THINKING RABBIT, who developed this game, to build 10 warehouses separately from commercial products]. PCマガジン (in Japanese). August 1983. pp. 52–56.
- "題して『倉庫番』PCマガジン番外編 (このプログラムは, PC-8801/9801 で使えます)" [Titled "Sokoban" PC Magazine Extra Edition (this program can be used with PC-8801 / 9801)]. PCマガジン (in Japanese). August 1983. pp. 52–56.
- Low, Lafe (November 1988). "News Line; Made in Japan". inCider (43). 14, 15.
- M. Fryers; M. T. Greene (1995). "Sokoban". Eureka (54).
- Dor, Dorit; Zwick, Uri (1999). "SOKOBAN and other motion planning problems". Computational Geometry. 13 (4): 215–228. doi:10.1016/S0925-7721(99)00017-6. ISSN 0925-7721.
- Joseph C. Culberson, Sokoban is PSPACE-complete (PS). Technical Report TR 97-02, Dept. of Computing Science, University of Alberta, 1997.
- David Holland and Yaron Shoham, "Theoretical analysis on Picokosmos 17".
- Andreas Junghanns, Jonathan Schaeffer (2001) Sokoban: Enhancing general single-agent search methods using domain knowledge, Artificial Intelligence 129(1–2):219–251 (Special issue on heuristic search in artificial intelligence).
- Junghanns, Andreas; Schaeffer, Jonathan (1997). "Sokoban: A Challenging Single-Agent Search Problem" (PDF). In IJCAI Workshop on Using Games as an Experimental Testbed for AI Research. University of Alberta. pp. 27–36.
- Yaron Shoham, Jonathan Shaeffer (2020) The FESS Algorithm: A Feature Based Approach to Single-Agent Search. Published in: 2020 IEEE Conference on Games (CoG)
- "Solver Statistics – Sokoban Wiki". Retrieved 8 February 2013.
- THE 倉庫番 (in Japanese). 1987. p. 113. ISBN 4-88239-606-8.