These approaches attempt to mitigate the problems of the game of Go having a high branching factor and numerous other difficulties. Computer Go research results are being applied to other similar fields such as cognitive science , pattern recognition and machine learning. The only choice a program needs to make is where to place its next stone. However, this decision is made difficult by the wide range of impacts a single stone can have across the entire board, and the complex interactions various stones' groups can have with each other.
Various architectures have arisen for handling this problem. The most popular use:. Few programs use only one of these techniques exclusively; most combine portions of each into one synthetic system. One traditional AI technique for creating game playing software is to use a minimax tree search. This involves playing out all hypothetical moves on the board up to a certain point, then using an evaluation function to estimate the value of that position for the current player.
The move which leads to the best hypothetical board is selected, and the process is repeated each turn. While tree searches have been very effective in computer chess , they have seen less success in Computer Go programs.
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This is partly because it has traditionally been difficult to create an effective evaluation function for a Go board, and partly because the large number of possible moves each side can make each leads to a high branching factor. This makes this technique very computationally expensive. There are several techniques, which can greatly improve the performance of search trees in terms of both speed and memory.
Pruning techniques such as alpha—beta pruning , Principal Variation Search , and MTD-f can reduce the effective branching factor without loss of strength. In tactical areas such as life and death, Go is particularly amenable to caching techniques such as transposition tables. These can reduce the amount of repeated effort, especially when combined with an iterative deepening approach. In order to quickly store a full-sized Go board in a transposition table, a hashing technique for mathematically summarizing is generally necessary.
Zobrist hashing is very popular in Go programs because it has low collision rates, and can be iteratively updated at each move with just two XORs , rather than being calculated from scratch. Even using these performance-enhancing techniques, full tree searches on a full-sized board are still prohibitively slow. Searches can be sped up by using large amounts of domain specific pruning techniques, such as not considering moves where your opponent is already strong, and selective extensions like always considering moves next to groups of stones which are about to be captured.
However, both of these options introduce a significant risk of not considering a vital move which would have changed the course of the game. Results of computer competitions show that pattern matching techniques for choosing a handful of appropriate moves combined with fast localized tactical searches explained above were once sufficient to produce a competitive program.
For example, GNU Go was competitive until Novices often learn a lot from the game records of old games played by master players. There is a strong hypothesis that suggests that acquiring Go knowledge is a key to making a strong computer Go. For example, Tim Kinger and David Mechner argue that "it is our belief that with better tools for representing and maintaining Go knowledge, it will be possible to develop stronger Go programs.
After implementation, the use of expert knowledge has been proved very effective in programming Go software. Hundreds of guidelines and rules of thumb for strong play have been formulated by both high-level amateurs and professionals.
The programmer's task is to take these heuristics , formalize them into computer code, and utilize pattern matching and pattern recognition algorithms to recognize when these rules apply. It is also important to have a system for determining what to do in the event that two conflicting guidelines are applicable. Most of the relatively successful results come from programmers' individual skills at Go and their personal conjectures about Go, but not from formal mathematical assertions; they are trying to make the computer mimic the way they play Go.
This method has until recently been the most successful technique in generating competitive Go programs on a full-sized board. Nevertheless, adding knowledge of Go sometimes weakens the program because some superficial knowledge might bring mistakes: "the best programs usually play good, master level moves.
However, as every games player knows, just one bad move can ruin a good game. Program performance over a full game can be much lower than master level. One major alternative to using hand-coded knowledge and searches is the use of Monte Carlo methods. This is done by generating a list of potential moves, and for each move playing out thousands of games at random on the resulting board. The move which leads to the best set of random games for the current player is chosen as the best move.
The advantage of this technique is that it requires very little domain knowledge or expert input, the trade-off being increased memory and processor requirements. However, because the moves used for evaluation are generated at random it is possible that a move which would be excellent except for one specific opponent response would be mistakenly evaluated as a good move. The result of this are programs which are strong in an overall strategic sense, but are imperfect tactically.
In , a new search technique, upper confidence bounds applied to trees UCT ,  was developed and applied to many 9x9 Monte-Carlo Go programs with excellent results. UCT uses the results of the play outs collected so far to guide the search along the more successful lines of play, while still allowing alternative lines to be explored. The UCT technique along with many other optimizations for playing on the larger 19x19 board has led MoGo to become one of the strongest research programs. While knowledge-based systems have been very effective at Go, their skill level is closely linked to the knowledge of their programmers and associated domain experts.
This is generally done by allowing a neural network or genetic algorithm to either review a large database of professional games, or play many games against itself or other people or programs. These algorithms are then able to utilize this data as a means of improving their performance. AlphaGo used this to great effect. Other programs using neural nets previously have been NeuroGo and WinHonte.
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Machine learning techniques can also be used in a less ambitious context to tune specific parameters of programs which rely mainly on other techniques. For example, Crazy Stone learns move generation patterns from several hundred sample games, using a generalization of the Elo rating system. AlphaGo , developed by Google DeepMind , made a significant advance by beating a professional human player in October , using techniques that combined deep learning and Monte-Carlo tree search. Several annual competitions take place between Go computer programs, the most prominent being the Go events at the Computer Olympiad.
They ran from to One of the early drivers of computer Go research was the Ing Prize, a relatively large money award sponsored by Taiwanese banker Ing Chang-ki , offered annually between and at the World Computer Go Congress or Ing Cup. The winner of this tournament was allowed to challenge young players at a handicap in a short match. If the computer won the match, the prize was awarded and a new prize announced: a larger prize for beating the players at a lesser handicap. The series of Ing prizes was set to expire either 1 in the year or 2 when a program could beat a 1-dan professional at no handicap for 40,, NT dollars.
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The last winner was Handtalk in , claiming , NT dollars for winning an stone handicap match against three 11—13 year old amateur 2—6 dans. At the time the prize expired in , the unclaimed prize was , NT dollars for winning a nine-stone handicap match. Many other large regional Go tournaments "congresses" had an attached computer Go event. Japan started sponsoring computer Go competitions in That tournament was supplanted by the Gifu Challenge, which was held annually from to in Ogaki, Gifu.
When two computers play a game of Go against each other, the ideal is to treat the game in a manner identical to two humans playing while avoiding any intervention from actual humans.
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However, this can be difficult during end game scoring. The main problem is that Go playing software, which usually communicates using the standardized Go Text Protocol GTP , will not always agree with respect to the alive or dead status of stones. While there is no general way for two different programs to "talk it out" and resolve the conflict, this problem is avoided for the most part by using Chinese , Tromp-Taylor , or American Go Association AGA rules in which continued play without penalty is required until there is no more disagreement on the status of any stones on the board. In practice, such as on the KGS Go Server, the server can mediate a dispute by sending a special GTP command to the two client programs indicating they should continue placing stones until there is no question about the status of any particular group all dead stones have been captured.
The CGOS Go Server usually sees programs resign before a game has even reached the scoring phase, but nevertheless supports a modified version of Tromp-Taylor rules requiring a full play out. It should be noted that these rule sets mean that a program which was in a winning position at the end of the game under Japanese rules when both players have passed could lose because of poor play in the resolution phase, but this is not a common occurrence and is considered a normal part of the game under all of the area rule sets.
The main drawback to the above system is that some rule sets such as the traditional Japanese rules penalize the players for making these extra moves, precluding the use of additional playout for two computers. Historically, another method for resolving this problem was to have an expert human judge the final board. However, this introduces subjectivity into the results and the risk that the expert would miss something the program saw.
Many programs are available that allow computer Go engines to play against each other and they almost always communicate via the Go Text Protocol GTP. GoGUI and its addon gogui-twogtp can be used to play two engines against each other on a single computer system. Go engine play as well as Go engine vs. CGOS is a dedicated computer vs. From Wikipedia, the free encyclopedia.
Field of artificial intelligence dedicated to creating a computer program that plays Go. This article is about the study of Go board game in artificial intelligence. For the computer programming language called Go, see Go programming language. Not to be confused with Go software.
For the American hip hop trio, see Migos. This section needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Main article: Monte-Carlo tree search. Main article: AlphaGo. See also: Go software. Artificial intelligence portal. May Artificial Intelligence. Retrieved 14 March Bibcode : Natur. Retrieved 11 December Retrieved Thesis pp. Retrieved 18 October Retrieved 23 October Retrieved 28 January Archived from the original on 14 August Go Game Guru.
Archived from the original on You can confirm the results of games played and the history of your rating. Export and import sgf game files You will be able to import and load game records in sgf format. If you save games in DL format, the results of the record analysis will also be saved in the file. I remembered this old thread and thought it might be usefull to update and revise, given some new programs and usefull apps coming to my attention now all in the first post.
If you do not think so just say nothing and we will let the thread die out forever. Unfortunatelly, right now that would only be possible by some fan made third party app. I know there is a quite functionalandroid app for OGS made by one of our players, but do not know anything about iOS. As far as we are concerned or more precisesly our devs - I just nod in agreement we are focused solely on improving the online browser experience at the moment.
Thanks for the overview, AdamR. Probably not. The menu on the right, on the page for a single game, renders terribly on Chrome for iPad. I often pause the game by accident when trying to set conditional moves or use the score estimator. AdamR November 18, , pm 1. Not sure I would recommend to beginners. Why are we not allowed to play games vs vastly different ranked bots even if it's not a ranked game?
Bots for noobs? Pempu December 28, , am 5. AdamR December 28, , am 6. CtrlC December 28, , pm 9. CtrlC December 28, , pm Sorry, my mistake. I use it on PC but I thought there was Mac option also. Kolibry January 2, , am If you dont mind paid apps and kinda pricey too , Crazy Stone Deep Learning is a really strong AI bot using neural network 5d I think , that allow a lot of features : 20 levels of play from 15k to 5d There are 20 levels of play 15k-5d for all the board sizes. AdamR May 9, , am I remembered this old thread and thought it might be usefull to update and revise, given some new programs and usefull apps coming to my attention now all in the first post If you do not think so just say nothing and we will let the thread die out forever.
BHydden May 9, , am AdamR May 9, , pm