Ladder (Go)

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style="text-align:center" | 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 {{Nihongo|ladder|四丁, シチョウ|shichō}}{{cite web | url = http://senseis.xmp.net/?Ladder | title = Ladder | author = Sensei's Library | access-date = 2008-02-15}},({{langx|zh|征子|zhengzi}}) 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.{{harvnb|Kim|Jeong|1997|pp=88–90}}{{harvnb|Shotwell|2003|loc=Chapter 8}}

A ladder can require reading 50 or more moves ahead, which even amateur players can do, as most of the moves are forced.{{cite journal|last=Müller|first=Martin|title=Computer Go|journal=Artificial Intelligence|volume=134|date=January 2002|pages=145-179|doi=10.1016/S0004-3702(01)00121-7|doi-access=}} 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.{{cite journal |first1=David |last1=Silver|author-link1=David Silver (programmer)|first2= Julian|last2= Schrittwieser|first3= Karen|last3= Simonyan|first4= Ioannis|last4= Antonoglou|first5= Aja|last5= Huang|author-link5=Aja Huang|first6=Arthur|last6= Guez|first7= Thomas|last7= Hubert|first8= Lucas|last8= Baker|first9= Matthew|last9= Lai|first10= Adrian|last10= Bolton|first11= Yutian|last11= Chen|author-link11=Chen Yutian|first12= Timothy|last12= Lillicrap|first13=Hui|last13= Fan|author-link13=Fan Hui|first14= Laurent|last14= Sifre|first15= George van den|last15= Driessche|first16= Thore|last16= Graepel|first17= Demis|last17= Hassabis |author-link17=Demis Hassabis|title=Mastering the game of Go without human knowledge|journal=Nature|issn= 0028-0836|pages=354–359|volume =550|issue =7676|doi =10.1038/nature24270|date=19 October 2017|bibcode=2017Natur.550..354S |pmid=29052630|s2cid=205261034|url=https://discovery.ucl.ac.uk/id/eprint/10045895/1/agz_unformatted_nature.pdf}}{{closed access}} Other Go AI such as AlphaGo (before AlphaGo Zero) or KataGo use information about ladder outcomes as input features of their neural nets.{{Cite journal|title = Mastering the game of Go with deep neural networks and tree search|journal = Nature| issn= 0028-0836|pages = 484–489|volume = 529|issue = 7587|doi = 10.1038/nature16961|pmid = 26819042|first1 = David|last1 = Silver|author-link1=David Silver (programmer)|first2 = Aja|last2 = Huang|author-link2=Aja Huang|first3 = Chris J.|last3 = Maddison|first4 = Arthur|last4 = Guez|first5 = Laurent|last5 = Sifre|first6 = George van den|last6 = Driessche|first7 = Julian|last7 = Schrittwieser|first8 = Ioannis|last8 = Antonoglou|first9 = Veda|last9 = Panneershelvam|first10= Marc|last10= Lanctot|first11= Sander|last11= Dieleman|first12=Dominik|last12= Grewe|first13= John|last13= Nham|first14= Nal|last14= Kalchbrenner|first15= Ilya|last15= Sutskever|author-link15=Ilya Sutskever|first16= Timothy|last16= Lillicrap|first17= Madeleine|last17= Leach|first18= Koray|last18= Kavukcuoglu|first19= Thore|last19= Graepel|first20= Demis |last20=Hassabis|author-link20=Demis Hassabis|date= 28 January 2016|bibcode = 2016Natur.529..484S|s2cid = 515925}}{{closed access}}{{citation|last=Wu|first=David J.|title=Accelerating Self-Play Learning in Go|arxiv=1902.10565|year=2020}}