A ladder shape for black (it is white's move). Black will win this ladder battle due to the marked black stone, which will put the white stone that will be played at the spot indicated by a into atari; if the marked stone did not exist, then white would inevitably win when the regular pattern of play extended to the edge of the board. A broken ladder is full of opportunities for a double atari on white, such as positions c and d.

In the game of Go, a ladder (四丁, シチョウ, shichō)[1],(征子) is a basic sequence of moves in which an attacker pursues a group in atari in a zig-zag pattern across the board. If there are no intervening stones, the group will hit the edge of the board and be captured.

The sequence is so basic that there is a Go proverb saying "if you don't know ladders, don't play Go."

The ladder tactic fails if there are stones supporting those being chased close enough to the diagonal path of the ladder. Such a failing ladder is called a broken ladder. Secondary double threat tactics around ladders, involving playing a stone in such a way as to break the ladder and also create some other possibility, are potentially very complex. Such a play is called a ladder breaker.[2][3]

A ladder can require reading 50 or more moves ahead, which even amateur players can do, as most of the moves are forced.[4] Although ladders are one of the first techniques which human players learn, AlphaGo Zero was only able to handle them much later in its training than many other Go concepts.[5] Other Go AI such as AlphaGo (before AlphaGo Zero) or KataGo use information about ladder outcomes as input features of their neural nets.[6][7]

See also

References

  1. ↑ Sensei's Library. "Ladder". Retrieved 2008-02-15.
  2. ↑ Kim & Jeong 1997, pp. 88–90
  3. ↑ Shotwell 2003, Chapter 8
  4. ↑ Müller, Martin (January 2002). "Computer Go". Artificial Intelligence. 134: 145–179. doi:10.1016/S0004-3702(01)00121-7.
  5. ↑ Silver, David; Schrittwieser, Julian; Simonyan, Karen; Antonoglou, Ioannis; Huang, Aja; Guez, Arthur; Hubert, Thomas; Baker, Lucas; Lai, Matthew; Bolton, Adrian; Chen, Yutian; Lillicrap, Timothy; Fan, Hui; Sifre, Laurent; Driessche, George van den; Graepel, Thore; Hassabis, Demis (19 October 2017). "Mastering the game of Go without human knowledge" (PDF). Nature. 550 (7676): 354–359. Bibcode:2017Natur.550..354S. doi:10.1038/nature24270. ISSN 0028-0836. PMID 29052630. S2CID 205261034.Closed access icon
  6. ↑ Silver, David; Huang, Aja; Maddison, Chris J.; Guez, Arthur; Sifre, Laurent; Driessche, George van den; Schrittwieser, Julian; Antonoglou, Ioannis; Panneershelvam, Veda; Lanctot, Marc; Dieleman, Sander; Grewe, Dominik; Nham, John; Kalchbrenner, Nal; Sutskever, Ilya; Lillicrap, Timothy; Leach, Madeleine; Kavukcuoglu, Koray; Graepel, Thore; Hassabis, Demis (28 January 2016). "Mastering the game of Go with deep neural networks and tree search". Nature. 529 (7587): 484–489. Bibcode:2016Natur.529..484S. doi:10.1038/nature16961. ISSN 0028-0836. PMID 26819042. S2CID 515925.Closed access icon
  7. ↑ Wu, David J. (2020), Accelerating Self-Play Learning in Go, arXiv:1902.10565

Further reading

  • Cho, Chikun (1997). Go: A Complete Introduction to the Game. Tokyo: Kiseido Publishing Company. ISBN 4-906574-50-5.
  • Kageyama, Toshiro (1978). Lessons in the Fundamentals of Go. Translated by Davies, James. Tokyo: Ishi Press.
  • Kim, Janice; Jeong, Soo-hyun (1997). Learn to Play Go: A Master's Guide to the Ultimate Game (2nd ed.). Good Move Press. ISBN 0-9644796-1-3.
  • Shotwell, Peter (2003). Go! More Than a Game. Tuttle Publishing. ISBN 978-1-4629-0006-0.
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