Difference between revisions of "Go"

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<span id="CNN"></span>
 
<span id="CNN"></span>
 
==CNNs==
 
==CNNs==
As mentioned by [[Ilya Sutskever]] and [[Vinod Nair]] in 2008 <ref>[[Ilya Sutskever]], [[Vinod Nair]] ('''2008'''). ''Mimicking Go Experts with Convolutional Neural Networks''. [http://dblp.uni-trier.de/db/conf/icann/icann2008-2.html#SutskeverN08 ICANN 2008], [http://www.cs.utoronto.ca/~ilya/pubs/2008/go_paper.pdf pdf]</ref>, [[Neural Networks#Convolutional|convolutional neural networks]] are well suited for problems with a [https://en.wikipedia.org/wiki/Translational_symmetry natural translation invariance], such as [https://en.wikipedia.org/wiki/Outline_of_object_recognition object recognition]. Go has some translation invariance, because if all the pieces on a hypothetical Go board are shifted to the left, then the best move will also shift (with the exception of pieces that are on the boundary of the board). Many applications of neural networks to Go have already used convolutional neural networks, such as [[Nicol N. Schraudolph]] et al. <ref>[[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1994'''). ''[http://nic.schraudolph.org/bib2html/b2hd-SchDaySej94.html Temporal Difference Learning of Position Evaluation in the Game of Go]''. [http://papers.nips.cc/book/advances-in-neural-information-processing-systems-6-1993 Advances in Neural Information Processing Systems 6]</ref>, [[Erik van der Werf]] et al. <ref>[[Erik van der Werf]], [[Jos Uiterwijk]], [[Eric Postma]], [[Jaap van den Herik]] ('''2002'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-540-40031-8_26 Local Move Prediction in Go]''. [[CG 2002]]</ref>, and [[Markus Enzenberger]] <ref>[[Markus Enzenberger]] ('''2003'''). ''[http://webdocs.cs.ualberta.ca/~emarkus/neurogo/neurogo3/index.html Evaluation in Go by a Neural Network using Soft Segmentation]''. [[Advances in Computer Games 10]], [http://webdocs.cs.ualberta.ca/~emarkus/publications/neurogo3.pdf pdf]</ref>, among others.  
+
As mentioned by [[Ilya Sutskever]] and [[Vinod Nair]] in 2008 <ref>[[Ilya Sutskever]], [[Vinod Nair]] ('''2008'''). ''Mimicking Go Experts with Convolutional Neural Networks''. [http://dblp.uni-trier.de/db/conf/icann/icann2008-2.html#SutskeverN08 ICANN 2008], [http://www.cs.utoronto.ca/~ilya/pubs/2008/go_paper.pdf pdf]</ref>, [[Neural Networks#Convolutional|convolutional neural networks]] are well suited for problems with a [https://en.wikipedia.org/wiki/Translational_symmetry natural translation invariance], such as [https://en.wikipedia.org/wiki/Outline_of_object_recognition object recognition]. Go has some translation invariance, because if all the pieces on a hypothetical Go board are shifted to the left, then the best move will also shift (with the exception of pieces that are on the boundary of the board). Many applications of neural networks to Go have already used convolutional neural networks, such as [[Nicol N. Schraudolph]] et al. <ref>[[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1993'''). ''[https://papers.nips.cc/paper/820-temporal-difference-learning-of-position-evaluation-in-the-game-of-go Temporal Difference Learning of Position Evaluation in the Game of Go]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-6-1993 NIPS 1993]</ref>, [[Erik van der Werf]] et al. <ref>[[Erik van der Werf]], [[Jos Uiterwijk]], [[Eric Postma]], [[Jaap van den Herik]] ('''2002'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-540-40031-8_26 Local Move Prediction in Go]''. [[CG 2002]]</ref>, and [[Markus Enzenberger]] <ref>[[Markus Enzenberger]] ('''2003'''). ''[http://webdocs.cs.ualberta.ca/~emarkus/neurogo/neurogo3/index.html Evaluation in Go by a Neural Network using Soft Segmentation]''. [[Advances in Computer Games 10]], [http://webdocs.cs.ualberta.ca/~emarkus/publications/neurogo3.pdf pdf]</ref>, among others.  
  
 
In 2014, two teams independently investigated whether deep convolutional neural networks <ref>[https://en.wikipedia.org/wiki/Convolutional_neural_network Convolutional neural network from Wikipedia]</ref> could be used to directly represent and [[Learning|learn]] a move evaluation function for the game of Go. [[Christopher Clark]] and [[Amos Storkey]] trained an 8-layer convolutional neural network by [[Supervised Learning|supervised learning]] from a database of human professional games, which without any [[Search|search]], defeated the traditional search program [[Gnu Go]]  in 86% of the games <ref>[[Christopher Clark]], [[Amos Storkey]] ('''2014'''). ''Teaching Deep Convolutional Neural Networks to Play Go''. [http://arxiv.org/abs/1412.3409 arXiv:1412.3409]</ref> <ref>[http://erikbern.com/2014/12/11/deep-learning-for-go/ Deep learning for… Go] by [[Erik Bernhardsson]], December 11, 2014</ref> <ref>[http://computer-go.org/pipermail/computer-go/2014-December/007010.html Teaching Deep Convolutional Neural Networks to Play Go] by [[Hiroshi Yamashita]],  [http://computer-go.org/pipermail/computer-go/ The Computer-go Archives], December 14, 2014</ref> <ref>[http://www.technologyreview.com/view/533496/why-neural-networks-look-set-to-thrash-the-best-human-go-players-for-the-first-time/ Why Neural Networks Look Set to Thrash the Best Human Go Players for the First Time] | [https://en.wikipedia.org/wiki/MIT_Technology_Review MIT Technology Review], December 15, 2014</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=54663 Teaching Deep Convolutional Neural Networks to Play Go] by [[Michel Van den Bergh]], [[CCC]], December 16, 2014</ref>. In their paper ''Move Evaluation in Go Using Deep Convolutional Neural Networks'' <ref>[[Chris J. Maddison]], [[Shih-Chieh Huang|Aja Huang]], [[Ilya Sutskever]], [[David Silver]] ('''2014'''). ''Move Evaluation in Go Using Deep Convolutional Neural Networks''. [http://arxiv.org/abs/1412.6564v1 arXiv:1412.6564v1]</ref>, [[Chris J. Maddison]], [[Shih-Chieh Huang|Aja Huang]], [[Ilya Sutskever]], and [[David Silver]] report they trained a large 12-layer convolutional neural network in a similar way, to beat Gnu Go in 97% of the games, and matched the performance of a state-of-the-art [[Monte-Carlo Tree Search]] that simulates a million positions per move <ref>[http://computer-go.org/pipermail/computer-go/2014-December/007046.html Move Evaluation in Go Using Deep Convolutional Neural Networks] by [[Shih-Chieh Huang|Aja Huang]], [http://computer-go.org/pipermail/computer-go/ The Computer-go Archives], December 19, 2014</ref>.   
 
In 2014, two teams independently investigated whether deep convolutional neural networks <ref>[https://en.wikipedia.org/wiki/Convolutional_neural_network Convolutional neural network from Wikipedia]</ref> could be used to directly represent and [[Learning|learn]] a move evaluation function for the game of Go. [[Christopher Clark]] and [[Amos Storkey]] trained an 8-layer convolutional neural network by [[Supervised Learning|supervised learning]] from a database of human professional games, which without any [[Search|search]], defeated the traditional search program [[Gnu Go]]  in 86% of the games <ref>[[Christopher Clark]], [[Amos Storkey]] ('''2014'''). ''Teaching Deep Convolutional Neural Networks to Play Go''. [http://arxiv.org/abs/1412.3409 arXiv:1412.3409]</ref> <ref>[http://erikbern.com/2014/12/11/deep-learning-for-go/ Deep learning for… Go] by [[Erik Bernhardsson]], December 11, 2014</ref> <ref>[http://computer-go.org/pipermail/computer-go/2014-December/007010.html Teaching Deep Convolutional Neural Networks to Play Go] by [[Hiroshi Yamashita]],  [http://computer-go.org/pipermail/computer-go/ The Computer-go Archives], December 14, 2014</ref> <ref>[http://www.technologyreview.com/view/533496/why-neural-networks-look-set-to-thrash-the-best-human-go-players-for-the-first-time/ Why Neural Networks Look Set to Thrash the Best Human Go Players for the First Time] | [https://en.wikipedia.org/wiki/MIT_Technology_Review MIT Technology Review], December 15, 2014</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=54663 Teaching Deep Convolutional Neural Networks to Play Go] by [[Michel Van den Bergh]], [[CCC]], December 16, 2014</ref>. In their paper ''Move Evaluation in Go Using Deep Convolutional Neural Networks'' <ref>[[Chris J. Maddison]], [[Shih-Chieh Huang|Aja Huang]], [[Ilya Sutskever]], [[David Silver]] ('''2014'''). ''Move Evaluation in Go Using Deep Convolutional Neural Networks''. [http://arxiv.org/abs/1412.6564v1 arXiv:1412.6564v1]</ref>, [[Chris J. Maddison]], [[Shih-Chieh Huang|Aja Huang]], [[Ilya Sutskever]], and [[David Silver]] report they trained a large 12-layer convolutional neural network in a similar way, to beat Gnu Go in 97% of the games, and matched the performance of a state-of-the-art [[Monte-Carlo Tree Search]] that simulates a million positions per move <ref>[http://computer-go.org/pipermail/computer-go/2014-December/007046.html Move Evaluation in Go Using Deep Convolutional Neural Networks] by [[Shih-Chieh Huang|Aja Huang]], [http://computer-go.org/pipermail/computer-go/ The Computer-go Archives], December 19, 2014</ref>.   
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* [[Walter R. Reitman]], [[James Kerwin]], [[Robert Nado]], [[Judith Spencer Olson|Judith S. Reitman]], [[Bruce Wilcox]] ('''1974'''). ''[http://dl.acm.org/citation.cfm?id=810391 Goals and Plans in a Program for Playing Go]''. Proceedings of the 29th [[ACM]] Conference, reprinted in [[David Levy]] (ed.) ('''1988'''). ''[http://www.springer.com/computer/ai/book/978-1-4613-8756-5 Computer Games II]''. [http://books.google.com/books?id=aaoqAAAAMAAJ&lpg=RA1-PA18&ots=BJ98MzIWZ2&dq=Robert%20Nado%2C%20University%20of%20Michigan&hl=de&pg=RA1-PA18#v=onepage&q&f=true google books]  
 
* [[Walter R. Reitman]], [[James Kerwin]], [[Robert Nado]], [[Judith Spencer Olson|Judith S. Reitman]], [[Bruce Wilcox]] ('''1974'''). ''[http://dl.acm.org/citation.cfm?id=810391 Goals and Plans in a Program for Playing Go]''. Proceedings of the 29th [[ACM]] Conference, reprinted in [[David Levy]] (ed.) ('''1988'''). ''[http://www.springer.com/computer/ai/book/978-1-4613-8756-5 Computer Games II]''. [http://books.google.com/books?id=aaoqAAAAMAAJ&lpg=RA1-PA18&ots=BJ98MzIWZ2&dq=Robert%20Nado%2C%20University%20of%20Michigan&hl=de&pg=RA1-PA18#v=onepage&q&f=true google books]  
 
* [[Walter R. Reitman]], [[Bruce Wilcox]] ('''1975'''). ''[http://dl.acm.org/citation.cfm?id=810263&dl=ACM&coll=DL&CFID=431288258&CFTOKEN=32228685 Perception and representation of spatial relations in a program for playing Go]''. Proceedings of the 30th [[ACM]] Conference, reprinted in [[David Levy]] (ed.) ('''1988'''). ''[http://www.springer.com/computer/ai/book/978-1-4613-8756-5 Computer Games II]''.  
 
* [[Walter R. Reitman]], [[Bruce Wilcox]] ('''1975'''). ''[http://dl.acm.org/citation.cfm?id=810263&dl=ACM&coll=DL&CFID=431288258&CFTOKEN=32228685 Perception and representation of spatial relations in a program for playing Go]''. Proceedings of the 30th [[ACM]] Conference, reprinted in [[David Levy]] (ed.) ('''1988'''). ''[http://www.springer.com/computer/ai/book/978-1-4613-8756-5 Computer Games II]''.  
 +
* [[David B. Benson]] ('''1976'''). ''Life in the Game of Go''. [https://en.wikipedia.org/wiki/Information_Sciences_(journal) Information Sciences], Vol. 10, [https://webdocs.cs.ualberta.ca/~games/go/seminar/2002/020717/benson.pdf pdf]
 
* [[Judith Spencer Olson|Judith S. Reitman]] ('''1976'''). ''[http://deepblue.lib.umich.edu/handle/2027.42/21741 Skilled Perception in Go: Deducing Memory Structures from Inter-Response Times]''. [http://www.journals.elsevier.com/cognitive-psychology/ Cognitive Psychology], Vol. 8, No, 3
 
* [[Judith Spencer Olson|Judith S. Reitman]] ('''1976'''). ''[http://deepblue.lib.umich.edu/handle/2027.42/21741 Skilled Perception in Go: Deducing Memory Structures from Inter-Response Times]''. [http://www.journals.elsevier.com/cognitive-psychology/ Cognitive Psychology], Vol. 8, No, 3
 
* [[Walter R. Reitman]], [[Bruce Wilcox]] ('''1977'''). ''[http://dl.acm.org/citation.cfm?id=1045396 Pattern Recognition and Pattern-Directed Inference in a Program for Playing Go]''. [[ACM#SIG|ACM SIGART Bulletin]], No. 63, reprinted in [[David Levy]] (ed.) ('''1988'''). ''[http://www.springer.com/computer/ai/book/978-1-4613-8756-5 Computer Games II]''.
 
* [[Walter R. Reitman]], [[Bruce Wilcox]] ('''1977'''). ''[http://dl.acm.org/citation.cfm?id=1045396 Pattern Recognition and Pattern-Directed Inference in a Program for Playing Go]''. [[ACM#SIG|ACM SIGART Bulletin]], No. 63, reprinted in [[David Levy]] (ed.) ('''1988'''). ''[http://www.springer.com/computer/ai/book/978-1-4613-8756-5 Computer Games II]''.
Line 116: Line 117:
 
* [[David Fotland]] ('''1993'''). ''[http://www.smart-games.com/knowpap.txt Knowledge representation in the Many Faces of Go]''.
 
* [[David Fotland]] ('''1993'''). ''[http://www.smart-games.com/knowpap.txt Knowledge representation in the Many Faces of Go]''.
 
* [[Bernd Brügmann]] ('''1993'''). ''Monte Carlo Go''. [http://www.ideanest.com/vegos/MonteCarloGo.pdf pdf] <ref>[http://www.cgl.ucsf.edu/home/pett/go/Programs/Gobble.html Gobble]</ref>
 
* [[Bernd Brügmann]] ('''1993'''). ''Monte Carlo Go''. [http://www.ideanest.com/vegos/MonteCarloGo.pdf pdf] <ref>[http://www.cgl.ucsf.edu/home/pett/go/Programs/Gobble.html Gobble]</ref>
 +
* [[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1993'''). ''[https://papers.nips.cc/paper/820-temporal-difference-learning-of-position-evaluation-in-the-game-of-go Temporal Difference Learning of Position Evaluation in the Game of Go]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-6-1993 NIPS 1993] <ref>[http://satirist.org/learn-game/systems/go-net.html Nici Schraudolph’s go networks], review by [[Jay Scott]]</ref>
 
'''1994'''
 
'''1994'''
 
* [[Elwyn Berlekamp]], [[David Wolfe]] ('''1994'''). ''[https://math.berkeley.edu/~berlek/cgt/gobook.html Mathematical Go - Chilling Gets the Last Point]''. [https://en.wikipedia.org/wiki/A_K_Peters,_Ltd. A K Peters Ltd.], also in paperback as ''Mathematical Go Endgames: Nightmares For the Professional Go Player''. [https://en.wikipedia.org/wiki/Ishi_Press Ishi Press] <ref>[http://senseis.xmp.net/?MathematicalGo Mathematical Go] from [http://senseis.xmp.net/?About Sensei's Library]</ref>
 
* [[Elwyn Berlekamp]], [[David Wolfe]] ('''1994'''). ''[https://math.berkeley.edu/~berlek/cgt/gobook.html Mathematical Go - Chilling Gets the Last Point]''. [https://en.wikipedia.org/wiki/A_K_Peters,_Ltd. A K Peters Ltd.], also in paperback as ''Mathematical Go Endgames: Nightmares For the Professional Go Player''. [https://en.wikipedia.org/wiki/Ishi_Press Ishi Press] <ref>[http://senseis.xmp.net/?MathematicalGo Mathematical Go] from [http://senseis.xmp.net/?About Sensei's Library]</ref>
* [[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1994'''). ''[http://nic.schraudolph.org/bib2html/b2hd-SchDaySej94.html Temporal Difference Learning of Position Evaluation in the Game of Go]''. [http://papers.nips.cc/book/advances-in-neural-information-processing-systems-6-1993 Advances in Neural Information Processing Systems 6] <ref>[http://satirist.org/learn-game/systems/go-net.html Nici Schraudolph’s go networks], review by [[Jay Scott]]</ref>
 
 
==1995 ...==
 
==1995 ...==
 
* <span id="CSTR339"></span>[[Jay Burmeister]], [[Janet Wiles]] ('''1995'''). ''[http://staff.itee.uq.edu.au/janetw/Computer%20Go/CS-TR-339.html CS-TR-339 Computer Go Tech Report]''. Departments of Computer Science and Psychology, [https://en.wikipedia.org/wiki/University_of_Queensland The University of Queensland], [https://en.wikipedia.org/wiki/St_Lucia,_Queensland QLD 4072], [https://en.wikipedia.org/wiki/Australia Australia] (permanently - under construction)
 
* <span id="CSTR339"></span>[[Jay Burmeister]], [[Janet Wiles]] ('''1995'''). ''[http://staff.itee.uq.edu.au/janetw/Computer%20Go/CS-TR-339.html CS-TR-339 Computer Go Tech Report]''. Departments of Computer Science and Psychology, [https://en.wikipedia.org/wiki/University_of_Queensland The University of Queensland], [https://en.wikipedia.org/wiki/St_Lucia,_Queensland QLD 4072], [https://en.wikipedia.org/wiki/Australia Australia] (permanently - under construction)
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* [[Markus Enzenberger]] ('''1996'''). ''[http://webdocs.cs.ualberta.ca/~emarkus/neurogo/neurogo1996.html The Integration of A Priori Knowledge into a Go Playing Neural Network]''.
 
* [[Markus Enzenberger]] ('''1996'''). ''[http://webdocs.cs.ualberta.ca/~emarkus/neurogo/neurogo1996.html The Integration of A Priori Knowledge into a Go Playing Neural Network]''.
 
* [[Martin Müller]], [[Ralph Gasser]] ('''1996'''). ''Experiments in Computer Go Endgames''. [http://library.msri.org/books/Book29/index.html Games of No Chance] edited by [[Richard J. Nowakowski]]
 
* [[Martin Müller]], [[Ralph Gasser]] ('''1996'''). ''Experiments in Computer Go Endgames''. [http://library.msri.org/books/Book29/index.html Games of No Chance] edited by [[Richard J. Nowakowski]]
* [[Martin Müller]], [[Elwyn Berlekamp]], [http://senseis.xmp.net/?Bill Bill Spight] ('''1996'''). ''Generalized thermography: Algorithms, implementation, and application to Go endgames''. Technical Report 96-030, ICSI Berkeley, 1996. [http://webdocs.cs.ualberta.ca/~mmueller/ps/tr-96-030a.ps.gz postscript]
+
* [[Martin Müller]], [[Elwyn Berlekamp]], [[Bill Spight]] ('''1996'''). ''Generalized thermography: Algorithms, implementation, and application to Go endgames''. Technical Report 96-030, ICSI Berkeley, 1996. [http://webdocs.cs.ualberta.ca/~mmueller/ps/tr-96-030a.ps.gz postscript]
 
* [[Bruno Bouzy]], [[Tristan Cazenave]] ('''1996'''). ''Shared concepts between complex systems and the game of Go''. [http://www.lamsade.dauphine.fr/~cazenave/papers/shared96.pdf pdf]
 
* [[Bruno Bouzy]], [[Tristan Cazenave]] ('''1996'''). ''Shared concepts between complex systems and the game of Go''. [http://www.lamsade.dauphine.fr/~cazenave/papers/shared96.pdf pdf]
 
'''1997'''
 
'''1997'''
 
* [[Tristan Cazenave]] ('''1997'''). ''Gogol (an Analytical Learning Program)''. [http://www.ijcai.org/past/ijcai-97/ IJCAI'97], [http://www.lamsade.dauphine.fr/~cazenave/papers/fost97.pdf pdf]
 
* [[Tristan Cazenave]] ('''1997'''). ''Gogol (an Analytical Learning Program)''. [http://www.ijcai.org/past/ijcai-97/ IJCAI'97], [http://www.lamsade.dauphine.fr/~cazenave/papers/fost97.pdf pdf]
 
* [[Bruno Bouzy]], [[Tristan Cazenave]] ('''1997'''). ''Using the Object Oriented Paradigm to Model Context in Computer Go''. Context'97, [http://www.lamsade.dauphine.fr/~cazenave/papers/context97.pdf pdf]
 
* [[Bruno Bouzy]], [[Tristan Cazenave]] ('''1997'''). ''Using the Object Oriented Paradigm to Model Context in Computer Go''. Context'97, [http://www.lamsade.dauphine.fr/~cazenave/papers/context97.pdf pdf]
 +
* [[David Fotland]], [[Atsushi Yoshikawa]] ('''1997'''). ''The 3rd Fost-Cup World-Open Computer-Go Championship''. [[ICGA Journal#20_4|ICGA Journal, Vol. 20, No. 4]]
 
'''1998'''
 
'''1998'''
 
* [[Keh-Hsun Chen|Ken Chen]] ('''1998'''). ''Heuristic Search in Go Game Tree'', Proceedings of Joint Conference on Information Sciences ’98, Vol. II, pp. 274-278. The Association for Intelligent Machinery, Inc. ISBN 0-9643456-7-6.
 
* [[Keh-Hsun Chen|Ken Chen]] ('''1998'''). ''Heuristic Search in Go Game Tree'', Proceedings of Joint Conference on Information Sciences ’98, Vol. II, pp. 274-278. The Association for Intelligent Machinery, Inc. ISBN 0-9643456-7-6.
Line 139: Line 141:
 
* [[Shinichi Sei]], [[Toshiaki Kawashima]] ('''1998'''). ''Memory-Based Approach in Go-program KATSUNARI''. Fujitsu Social Science Laboratory, [http://usapyon.game.coocan.jp/katsunari/paper/cg98ws.pdf pdf]
 
* [[Shinichi Sei]], [[Toshiaki Kawashima]] ('''1998'''). ''Memory-Based Approach in Go-program KATSUNARI''. Fujitsu Social Science Laboratory, [http://usapyon.game.coocan.jp/katsunari/paper/cg98ws.pdf pdf]
 
* [[Morihiko Tajima]], [[Noriaki Sanechika]] ('''1998'''). ''[http://link.springer.com/chapter/10.1007/3-540-48957-6_18 Estimating the Possible Omission Number for Groups in Go by the Number of n -th Dame]''. [[CG 1998]]
 
* [[Morihiko Tajima]], [[Noriaki Sanechika]] ('''1998'''). ''[http://link.springer.com/chapter/10.1007/3-540-48957-6_18 Estimating the Possible Omission Number for Groups in Go by the Number of n -th Dame]''. [[CG 1998]]
 +
* [[Norman Richards]], [[David E. Moriarty]], [[Risto Miikkulainen]] ('''1998'''). ''[http://nn.cs.utexas.edu/?richards:apin98 Evolving Neural Networks to Play Go]''. [https://www.springer.com/journal/10489 Applied Intelligence], Vol. 8, No. 1
 
'''1999'''
 
'''1999'''
 
* [[Keh-Hsun Chen|Ken Chen]], [[Zhixing Chen]] ('''1999'''). ''Static Analysis of Life and Death in the Game of Go''. [https://www.journals.elsevier.com/information-sciences Information Sciences],  Vol. 121, Nos. 1-2, [https://webdocs.cs.ualberta.ca/~games/go/seminar/2002/020703/ld.pdf pdf]
 
* [[Keh-Hsun Chen|Ken Chen]], [[Zhixing Chen]] ('''1999'''). ''Static Analysis of Life and Death in the Game of Go''. [https://www.journals.elsevier.com/information-sciences Information Sciences],  Vol. 121, Nos. 1-2, [https://webdocs.cs.ualberta.ca/~games/go/seminar/2002/020703/ld.pdf pdf]
Line 155: Line 158:
 
* [[Bruno Bouzy]] ('''2001'''). ''Go Patterns Generated by Retrograde Analysis''. [[6th Computer Olympiad#Workshop|6th Computer Olympiad Workshop]], [http://www.mi.parisdescartes.fr/~bouzy/publications/RAGO.pdf pdf]
 
* [[Bruno Bouzy]] ('''2001'''). ''Go Patterns Generated by Retrograde Analysis''. [[6th Computer Olympiad#Workshop|6th Computer Olympiad Workshop]], [http://www.mi.parisdescartes.fr/~bouzy/publications/RAGO.pdf pdf]
 
* [[Erik van der Werf]], [[Jaap van den Herik]] ('''2001'''). ''Visual Learning in Go''. [[6th Computer Olympiad#Workshop|6th Computer Olympiad Workshop]], [http://erikvanderwerf.tengen.nl/pubdown/visual_learning_in_go.pdf pdf]
 
* [[Erik van der Werf]], [[Jaap van den Herik]] ('''2001'''). ''Visual Learning in Go''. [[6th Computer Olympiad#Workshop|6th Computer Olympiad Workshop]], [http://erikvanderwerf.tengen.nl/pubdown/visual_learning_in_go.pdf pdf]
* [[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]]  ('''2001'''). ''[http://nic.schraudolph.org/bib2html/b2hd-SchDaySej01.html Learning to Evaluate Go Positions via Temporal Difference Methods]''. in  [[Norio Baba]], [[Lakhmi C. Jain]] (eds.) ('''2001'''). ''[http://jasss.soc.surrey.ac.uk/7/1/reviews/takama.html Computational Intelligence in Games, Studies in Fuzziness and Soft Computing]''. [http://www.springer.com/economics?SGWID=1-165-6-73481-0 Physica-Verlag], revised version of [[Nicol N. Schraudolph#1994|1994 paper]]
+
* [[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]]  ('''2001'''). ''[http://nic.schraudolph.org/bib2html/b2hd-SchDaySej01.html Learning to Evaluate Go Positions via Temporal Difference Methods]''. [http://jasss.soc.surrey.ac.uk/7/1/reviews/takama.html Computational Intelligence in Games, Studies in Fuzziness and Soft Computing], [http://www.springer.com/economics?SGWID=1-165-6-73481-0 Physica-Verlag], revised version of [[Nicol N. Schraudolph#1993|1993 paper]]
 
* [[Tristan Cazenave]] ('''2001'''). ''Generating Patterns with External Conditions for the Game of Go''. [[Advances in Computer Games 9]], [http://www.lamsade.dauphine.fr/~cazenave/papers/acg9-final.pdf pdf]
 
* [[Tristan Cazenave]] ('''2001'''). ''Generating Patterns with External Conditions for the Game of Go''. [[Advances in Computer Games 9]], [http://www.lamsade.dauphine.fr/~cazenave/papers/acg9-final.pdf pdf]
 
* [[Jeng-Chi Yan]], [[Shun-Chin Hsu]] ('''2001'''). ''A Positional Judgment System for computer Go''. [[Advances in Computer Games 9]]
 
* [[Jeng-Chi Yan]], [[Shun-Chin Hsu]] ('''2001'''). ''A Positional Judgment System for computer Go''. [[Advances in Computer Games 9]]
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* [[Markus Enzenberger]] ('''2003'''). ''[http://webdocs.cs.ualberta.ca/~emarkus/neurogo/neurogo3/index.html Evaluation in Go by a Neural Network using Soft Segmentation]''. [[Advances in Computer Games 10]], [http://webdocs.cs.ualberta.ca/~emarkus/publications/neurogo3.pdf pdf]
 
* [[Markus Enzenberger]] ('''2003'''). ''[http://webdocs.cs.ualberta.ca/~emarkus/neurogo/neurogo3/index.html Evaluation in Go by a Neural Network using Soft Segmentation]''. [[Advances in Computer Games 10]], [http://webdocs.cs.ualberta.ca/~emarkus/publications/neurogo3.pdf pdf]
 
* [[Akihiro Kishimoto]], [[Martin Müller]]. ('''2003'''). ''Df-pn in Go: An Application to the One-Eye Problem''. [[Advances in Computer Games 10]], [http://www.fun.ac.jp/%7Ekishi/pdf_file/acg_kishimoto_mueller.pdf pdf] » [[Proof-Number Search]] <ref>[http://senseis.xmp.net/?Tsumego Tsumego at Sensei's Library]</ref>
 
* [[Akihiro Kishimoto]], [[Martin Müller]]. ('''2003'''). ''Df-pn in Go: An Application to the One-Eye Problem''. [[Advances in Computer Games 10]], [http://www.fun.ac.jp/%7Ekishi/pdf_file/acg_kishimoto_mueller.pdf pdf] » [[Proof-Number Search]] <ref>[http://senseis.xmp.net/?Tsumego Tsumego at Sensei's Library]</ref>
 +
* [[Erik van der Werf]], [[Jaap van den Herik]], [[Jos Uiterwijk]] ('''2003'''). ''Learning to Score Final Positions in the Game of Go''. [[Advances in Computer Games 10]], [http://erikvanderwerf.tengen.nl/pubdown/learning_to_score.pdf pdf]
 
* [[Bruno Bouzy]], [[Bernard Helmstetter]] ('''2003'''). ''Monte Carlo Go Developments''. [[Advances in Computer Games 10]], [http://www.ai.univ-paris8.fr/~bh/articles/acg10-mcgo.pdf pdf]
 
* [[Bruno Bouzy]], [[Bernard Helmstetter]] ('''2003'''). ''Monte Carlo Go Developments''. [[Advances in Computer Games 10]], [http://www.ai.univ-paris8.fr/~bh/articles/acg10-mcgo.pdf pdf]
 
* [[Bruno Bouzy]] ('''2003'''). ''Mathematical morphology applied to computer go''. IJPRAI Vol. 17, No. 2
 
* [[Bruno Bouzy]] ('''2003'''). ''Mathematical morphology applied to computer go''. IJPRAI Vol. 17, No. 2
Line 198: Line 202:
 
* [[Erik van der Werf]] ('''2004'''). ''AI techniques for the game of Go.'' Ph.D. thesis, [[Maastricht University|Universiteit Maastricht]], Maastricht, The Netherlands. ISBN 90 5278 445 0, [http://erikvanderwerf.tengen.nl/pubdown/thesis_erikvanderwerf.pdf pdf]
 
* [[Erik van der Werf]] ('''2004'''). ''AI techniques for the game of Go.'' Ph.D. thesis, [[Maastricht University|Universiteit Maastricht]], Maastricht, The Netherlands. ISBN 90 5278 445 0, [http://erikvanderwerf.tengen.nl/pubdown/thesis_erikvanderwerf.pdf pdf]
 
* [[Bruno Bouzy]] ('''2004'''). ''Toward a go player computational model.'' Habilitation to Supervise Research document, [https://en.wikipedia.org/wiki/Paris_VI_University Paris 6 University]
 
* [[Bruno Bouzy]] ('''2004'''). ''Toward a go player computational model.'' Habilitation to Supervise Research document, [https://en.wikipedia.org/wiki/Paris_VI_University Paris 6 University]
 +
* [[Kenneth O. Stanley]], [[Risto Miikkulainen]] ('''2004'''). ''[http://nn.cs.utexas.edu/?stanley:gecco04 Evolving a Roving Eye for Go]''. [https://dblp.uni-trier.de/db/conf/gecco/gecco2004-2.html GECCO 2004]
 
==2005 ...==
 
==2005 ...==
 
* [[Tristan Cazenave]], [[Bernard Helmstetter]] ('''2005'''). ''Combining tactical search and Monte-Carlo in the game of Go''. IEEE [http://www.informatik.uni-trier.de/~ley/db/conf/cig/cig2005.html#CazenaveH05 CIG 2005], [http://www.ai.univ-paris8.fr/~bh/articles/searchmcgo.pdf pdf], [http://www.lamsade.dauphine.fr/~cazenave/papers/searchmcgo.pdf pdf]
 
* [[Tristan Cazenave]], [[Bernard Helmstetter]] ('''2005'''). ''Combining tactical search and Monte-Carlo in the game of Go''. IEEE [http://www.informatik.uni-trier.de/~ley/db/conf/cig/cig2005.html#CazenaveH05 CIG 2005], [http://www.ai.univ-paris8.fr/~bh/articles/searchmcgo.pdf pdf], [http://www.lamsade.dauphine.fr/~cazenave/papers/searchmcgo.pdf pdf]
Line 207: Line 212:
 
* [[Bruno Bouzy]] ('''2005'''). ''Associating domain-dependent knowledge and Monte Carlo approaches within a go program''. Information Sciences, Heuristic Search and Computer Game Playing IV
 
* [[Bruno Bouzy]] ('''2005'''). ''Associating domain-dependent knowledge and Monte Carlo approaches within a go program''. Information Sciences, Heuristic Search and Computer Game Playing IV
 
* [[Thomas Philip Runarsson]], [[Simon Lucas]] ('''2005'''). ''Coevolution versus self-play temporal difference learning for acquiring position evaluation in small-board go''. [[IEEE#EC|IEEE Transactions on Evolutionary Computation]], Vol. 9, No. 6
 
* [[Thomas Philip Runarsson]], [[Simon Lucas]] ('''2005'''). ''Coevolution versus self-play temporal difference learning for acquiring position evaluation in small-board go''. [[IEEE#EC|IEEE Transactions on Evolutionary Computation]], Vol. 9, No. 6
 +
* [[Erik van der Werf]], [[Mark Winands]], [[Jaap van den Herik]], [[Jos Uiterwijk]] ('''2005'''). ''Learning to predict life and death from Go game records''. [https://en.wikipedia.org/wiki/Information_Sciences_(journal) Information Sciences], Vol. 175, No. 4
 +
* [[Erik van der Werf]], [[Jaap van den Herik]], [[Jos Uiterwijk]] ('''2005'''). ''Learning to score final positions in the game of Go''. [https://en.wikipedia.org/wiki/Theoretical_Computer_Science_(journal) Theoretical Computer Science], Vol. 349, No. 2, [http://erikvanderwerf.tengen.nl/pubdown/learning_to_score_extended.pdf pdf preprint]
 
'''2006'''
 
'''2006'''
 
* [[Keh-Hsun Chen|Ken Chen]], [[Peigang Zhang]] ('''2006'''). ''[http://link.springer.com/chapter/10.1007/978-3-540-75538-8_3 A New Heuristic Search Algorithm for Capturing Problems in Go]''. [[CG 2006]]
 
* [[Keh-Hsun Chen|Ken Chen]], [[Peigang Zhang]] ('''2006'''). ''[http://link.springer.com/chapter/10.1007/978-3-540-75538-8_3 A New Heuristic Search Algorithm for Capturing Problems in Go]''. [[CG 2006]]
Line 246: Line 253:
 
* [[Esa A. Seuranen]] ('''2009'''). ''Entropy in Go''. [[ICGA Journal#32_1|ICGA Journal, Vol. 32, No. 1]]  
 
* [[Esa A. Seuranen]] ('''2009'''). ''Entropy in Go''. [[ICGA Journal#32_1|ICGA Journal, Vol. 32, No. 1]]  
 
* [[Mark Winands]] ('''2009'''). ''Many Faces of Go wins 9x9 and 19x19 Go tournaments''. [[ICGA Journal#32_1|ICGA Journal, Vol. 32, No. 1]] » [[13th Computer Olympiad#Go|13th Computer Olympiad]]
 
* [[Mark Winands]] ('''2009'''). ''Many Faces of Go wins 9x9 and 19x19 Go tournaments''. [[ICGA Journal#32_1|ICGA Journal, Vol. 32, No. 1]] » [[13th Computer Olympiad#Go|13th Computer Olympiad]]
* [[Markus Enzenberger]], [[Martin Müller]] ('''2009'''). ''[http://www.cs.ualberta.ca/research/theses-publications/technical-reports/2009/tr09-08 Fuego - An Open-source Framework for Board Games and Go Engine Based on Monte-Carlo Tree Search]''. [http://www.cs.ualberta.ca/system/files/tech_report/2009/TR09-08.pdf pdf]
+
* [[Markus Enzenberger]], [[Martin Müller]] ('''2009'''). ''Fuego - An Open-source Framework for Board Games and Go Engine Based on Monte-Carlo Tree Search''. Technical Report TR 09-08, [[University of Alberta]]
* [[Martin Müller]] ('''2009'''). ''[https://era.library.ualberta.ca/files/vm40xs882 Fuego at the Computer Olympiad in Pamplona 2009: A Tournament Report]''. TR09-09, [https://pdfs.semanticscholar.org/8fba/eb0b4773040c1ff86e0796843555a716da6a.pdf pdf]
+
* [[Martin Müller]] ('''2009'''). ''[https://era.library.ualberta.ca/items/d5526cc7-d4c6-4e31-bdfc-6845def21e9e Fuego at the Computer Olympiad in Pamplona 2009: A Tournament Report]''. TR09-09
 
* [[Rémi Coulom]] ('''2009'''). ''The Monte-Carlo Revolution in Go''. JFFoS'2008: Japanese-French Frontiers of Science Symposium, [http://remi.coulom.free.fr/JFFoS/JFFoS.pdf slides as pdf]
 
* [[Rémi Coulom]] ('''2009'''). ''The Monte-Carlo Revolution in Go''. JFFoS'2008: Japanese-French Frontiers of Science Symposium, [http://remi.coulom.free.fr/JFFoS/JFFoS.pdf slides as pdf]
 
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Guillaume Chaslot]], [[Jean-Baptiste Hoock]], [[Arpad Rimmel]], [[Olivier Teytaud]], [[Shang-Rong Tsai]], [[Shun-Chin Hsu]], [[Tzung-Pei Hong]] ('''2009'''). ''[http://hal.inria.fr/inria-00369786/ The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments]''. [http://hal.inria.fr/docs/00/36/97/86/PDF/TCIAIG-2008-0010_Accepted_.pdf pdf]
 
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Guillaume Chaslot]], [[Jean-Baptiste Hoock]], [[Arpad Rimmel]], [[Olivier Teytaud]], [[Shang-Rong Tsai]], [[Shun-Chin Hsu]], [[Tzung-Pei Hong]] ('''2009'''). ''[http://hal.inria.fr/inria-00369786/ The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments]''. [http://hal.inria.fr/docs/00/36/97/86/PDF/TCIAIG-2008-0010_Accepted_.pdf pdf]
 
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Tzung-Pei Hong]], [[Guillaume Chaslot]], [[Jean-Baptiste Hoock]], [[Arpad Rimmel]], [[Olivier Teytaud]], [[Yau-Hwang Kuo]] ('''2009'''). ''A Novel Ontology for Computer Go Knowledge Management''. IEEE FUZZ (2009), [http://hal.inria.fr/docs/00/38/64/76/PDF/fuzz.pdf pdf]
 
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Tzung-Pei Hong]], [[Guillaume Chaslot]], [[Jean-Baptiste Hoock]], [[Arpad Rimmel]], [[Olivier Teytaud]], [[Yau-Hwang Kuo]] ('''2009'''). ''A Novel Ontology for Computer Go Knowledge Management''. IEEE FUZZ (2009), [http://hal.inria.fr/docs/00/38/64/76/PDF/fuzz.pdf pdf]
 
* [[Vincent Berthier]], [[Amine Bourki]], [[Matthieu Coulm]], [[Guillaume Chaslot]], [[Christophe Fiter]], [[Sylvain Gelly]], [[Jean-Baptiste Hoock]], [[Rémi Munos]], [[Julien Pérez]], [[Arpad Rimmel]], [[Philippe Rolet]], [[Olivier Teytaud]], [[Paul Vayssière]], [[Yizao Wang]], [[Ziqin Yu]] (et al.) ('''2009'''). ''Computer-Go is not only for Go''. Korea, August 2009 [http://www.lri.fr/~teytaud/korea.pdf slides as pdf]
 
* [[Vincent Berthier]], [[Amine Bourki]], [[Matthieu Coulm]], [[Guillaume Chaslot]], [[Christophe Fiter]], [[Sylvain Gelly]], [[Jean-Baptiste Hoock]], [[Rémi Munos]], [[Julien Pérez]], [[Arpad Rimmel]], [[Philippe Rolet]], [[Olivier Teytaud]], [[Paul Vayssière]], [[Yizao Wang]], [[Ziqin Yu]] (et al.) ('''2009'''). ''Computer-Go is not only for Go''. Korea, August 2009 [http://www.lri.fr/~teytaud/korea.pdf slides as pdf]
 +
* [[Erik van der Werf]], [[Mark Winands]] ('''2009'''). ''Solving Go for Rectangular Boards''. [[ICGA Journal#32_2|ICGA Journal, Vol. 32, No. 2]]
 
* [[Keh-Hsun Chen|Ken Chen]] ('''2009'''). ''Fuego wins 9x9 Go tournaments''. [[ICGA Journal#32_2|ICGA Journal, Vol. 32, No. 2]] » [[14th Computer Olympiad#Go9x9|14th Computer Olympiad]]
 
* [[Keh-Hsun Chen|Ken Chen]] ('''2009'''). ''Fuego wins 9x9 Go tournaments''. [[ICGA Journal#32_2|ICGA Journal, Vol. 32, No. 2]] » [[14th Computer Olympiad#Go9x9|14th Computer Olympiad]]
 
* [[Keh-Hsun Chen|Ken Chen]] ('''2009'''). ''Zen wins 19x19 Go tournaments''. [[ICGA Journal#32_2|ICGA Journal, Vol. 32, No. 2]] » [[14th Computer Olympiad#Go|14th Computer Olympiad]]
 
* [[Keh-Hsun Chen|Ken Chen]] ('''2009'''). ''Zen wins 19x19 Go tournaments''. [[ICGA Journal#32_2|ICGA Journal, Vol. 32, No. 2]] » [[14th Computer Olympiad#Go|14th Computer Olympiad]]
Line 257: Line 265:
 
* [[Keh-Hsun Chen|Ken Chen]], [[Dawei Du]], [[Peigang Zhang]] ('''2009'''). ''Monte-Carlo Tree Search and Computer Go''. [http://www.informatik.uni-trier.de/~ley/db/series/sci/sci251.html#ChenDZ09 Advances in Information and Intelligent Systems 2009]
 
* [[Keh-Hsun Chen|Ken Chen]], [[Dawei Du]], [[Peigang Zhang]] ('''2009'''). ''Monte-Carlo Tree Search and Computer Go''. [http://www.informatik.uni-trier.de/~ley/db/series/sci/sci251.html#ChenDZ09 Advances in Information and Intelligent Systems 2009]
 
* [[Seth Pellegrino]], [[Andrew Hubbard]], [[Jason Galbraith]], [[Peter D. Drake]], [[Yung-Pin Chen]] ('''2009'''). ''Localizing Search in Monte-Carlo Go using Statistical Covariance.'' [[ICGA Journal#32_3|ICGA Journal, Vol. 32, No. 3]]
 
* [[Seth Pellegrino]], [[Andrew Hubbard]], [[Jason Galbraith]], [[Peter D. Drake]], [[Yung-Pin Chen]] ('''2009'''). ''Localizing Search in Monte-Carlo Go using Statistical Covariance.'' [[ICGA Journal#32_3|ICGA Journal, Vol. 32, No. 3]]
* [[Thomas Wolf]] ('''2009'''). ''A library of eyes in Go, I: A life & death definition consistent with `bent-4'''. in [[Michael H. Albert]], [[Richard J. Nowakowski]] (eds) ('''2009'''). ''[https://www.goodreads.com/book/show/11519909-games-of-no-chance-3 Games of No Chance 3]''. [https://en.wikipedia.org/wiki/Cambridge_University_Press Cambridge University Press], [http://library.msri.org/books/Book56/files/26wolf.pdf pdf]
+
* [[Thomas Wolf]] ('''2009'''). ''A library of eyes in Go, I: A life & death definition consistent with `bent-4''. in [[Michael H. Albert]], [[Richard J. Nowakowski]] (eds) ('''2009'''). ''[https://www.goodreads.com/book/show/11519909-games-of-no-chance-3 Games of No Chance 3]''. [https://en.wikipedia.org/wiki/Cambridge_University_Press Cambridge University Press], [http://library.msri.org/books/Book56/files/26wolf.pdf pdf]
 
* [[Thomas Wolf]], [[Matthew Pratola]] ('''2009'''). ''A library of eyes in Go, II: Monolithic eyes''. in [[Michael H. Albert]], [[Richard J. Nowakowski]] (eds) ('''2009'''). ''[https://www.goodreads.com/book/show/11519909-games-of-no-chance-3 Games of No Chance 3]''. [https://en.wikipedia.org/wiki/Cambridge_University_Press Cambridge University Press], [http://library.msri.org/books/Book56/files/27wolf.pdf pdf]
 
* [[Thomas Wolf]], [[Matthew Pratola]] ('''2009'''). ''A library of eyes in Go, II: Monolithic eyes''. in [[Michael H. Albert]], [[Richard J. Nowakowski]] (eds) ('''2009'''). ''[https://www.goodreads.com/book/show/11519909-games-of-no-chance-3 Games of No Chance 3]''. [https://en.wikipedia.org/wiki/Cambridge_University_Press Cambridge University Press], [http://library.msri.org/books/Book56/files/27wolf.pdf pdf]
 
==2010 ...==
 
==2010 ...==
Line 268: Line 276:
 
* [[Hendrik Baier]], [[Peter D. Drake]] ('''2010'''). ''The power of forgetting: Improving the last-good-reply policy in Monte Carlo Go''. [[IEEE#TOCIAIGAMES|IEEE Transactions on Computational Intelligence and AI in Games]], Vol. 2, No. 4
 
* [[Hendrik Baier]], [[Peter D. Drake]] ('''2010'''). ''The power of forgetting: Improving the last-good-reply policy in Monte Carlo Go''. [[IEEE#TOCIAIGAMES|IEEE Transactions on Computational Intelligence and AI in Games]], Vol. 2, No. 4
 
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Shi-Jim Yen]], [[Yu-Jen Chen]], [[Cheng-Wei Chou]], [[Guillaume Chaslot]], [[Jean-Baptiste Hoock]], [[Arpad Rimmel]], [[Hassen Doghmen]] ('''2010'''). ''An ontology-based fuzzy inference system for computer Go applications''. [http://www.ijfs.org.tw/ International Journal of Fuzzy Systems], Vol. 12, No. 2, [https://www.researchgate.net/profile/Cheng_Wei_Chou/publication/265231387_An_Ontology-based_Fuzzy_Inference_System_for_Computer_Go_Applications/links/5498343f0cf2c5a7e342a903.pdf pdf]
 
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Shi-Jim Yen]], [[Yu-Jen Chen]], [[Cheng-Wei Chou]], [[Guillaume Chaslot]], [[Jean-Baptiste Hoock]], [[Arpad Rimmel]], [[Hassen Doghmen]] ('''2010'''). ''An ontology-based fuzzy inference system for computer Go applications''. [http://www.ijfs.org.tw/ International Journal of Fuzzy Systems], Vol. 12, No. 2, [https://www.researchgate.net/profile/Cheng_Wei_Chou/publication/265231387_An_Ontology-based_Fuzzy_Inference_System_for_Computer_Go_Applications/links/5498343f0cf2c5a7e342a903.pdf pdf]
 +
* [[Jean-Baptiste Hoock]], [[Chang-Shing Lee]], [[Arpad Rimmel]], [[Fabien Teytaud]], [[Mei-Hui Wang]], [[Olivier Teytaud]] ('''2010'''). ''Intelligent Agents for the Game of Go''. [[IEEE#CIM|IEEE Computational Intelligence Magazine]], Vol. 5, [https://hal.inria.fr/inria-00544758v2/document pdf]
 
'''2011'''
 
'''2011'''
 +
* [[Markus Enzenberger]], [[Martin Müller]], [[Broderick Arneson]], [[Richard Segal]] ('''2011'''). ''Fuego - An Open-source Framework for Board Games and Go Engine Based on Monte-Carlo Tree Search''. [[IEEE#TOCIAIGAMES|IEEE Transactions on Computational Intelligence and AI in Games]], Vol. 2, No. 4, [https://webdocs.cs.ualberta.ca/~mmueller/ps/fuego-TCIAIG.pdf pdf]
 
* [[Abdallah Saffidine]] ('''2011'''). ''Moccos wins the Panthom-Go Tournament''. [[ICGA Journal#34_1|ICGA Journal, Vol. 34, No. 1]] » [[15th Computer Olympiad#PanthomGo|15th Computer Olympiad]]
 
* [[Abdallah Saffidine]] ('''2011'''). ''Moccos wins the Panthom-Go Tournament''. [[ICGA Journal#34_1|ICGA Journal, Vol. 34, No. 1]] » [[15th Computer Olympiad#PanthomGo|15th Computer Olympiad]]
 
* [[Ingo Althöfer]] ('''2011'''). ''John Tromp in the Style of David Levy: 4-0 win in the Go bet''. [[ICGA Journal#34_2|ICGA Journal, Vol. 34, No. 2]] » [[John Tromp]] <ref>[http://dcook.org/gobet/ The Shodan Go Bet]</ref>
 
* [[Ingo Althöfer]] ('''2011'''). ''John Tromp in the Style of David Levy: 4-0 win in the Go bet''. [[ICGA Journal#34_2|ICGA Journal, Vol. 34, No. 2]] » [[John Tromp]] <ref>[http://dcook.org/gobet/ The Shodan Go Bet]</ref>
Line 299: Line 309:
 
* [[Katja Grace]] ('''2013'''). ''Algorithmic Progress in Six Domains''. Technical report 2013-3, [https://en.wikipedia.org/wiki/Machine_Intelligence_Research_Institute Machine Intelligence Research Institute], [https://en.wikipedia.org/wiki/Berkeley,_California Berkeley, CA], [http://intelligence.org/files/AlgorithmicProgress.pdf pdf], 5 [[Games|Game Playing]], 5.1 [[Chess]], 5.2 [[Go]], 9 [[Learning|Machine Learning]]
 
* [[Katja Grace]] ('''2013'''). ''Algorithmic Progress in Six Domains''. Technical report 2013-3, [https://en.wikipedia.org/wiki/Machine_Intelligence_Research_Institute Machine Intelligence Research Institute], [https://en.wikipedia.org/wiki/Berkeley,_California Berkeley, CA], [http://intelligence.org/files/AlgorithmicProgress.pdf pdf], 5 [[Games|Game Playing]], 5.1 [[Chess]], 5.2 [[Go]], 9 [[Learning|Machine Learning]]
 
* [[Chun-Hsiang Hsieh]], [[Jeng-Chi Yan]] ('''2013'''). ''[http://www.ijiet.org/show-39-348-1.html The Design and Development of a 9 by 9 GO Opening Game Knowledge Base System]''. [http://www.ijiet.org/ International Journal of Information and Education Technology], Vol. 3, No. 4
 
* [[Chun-Hsiang Hsieh]], [[Jeng-Chi Yan]] ('''2013'''). ''[http://www.ijiet.org/show-39-348-1.html The Design and Development of a 9 by 9 GO Opening Game Knowledge Base System]''. [http://www.ijiet.org/ International Journal of Information and Education Technology], Vol. 3, No. 4
 +
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Yu-Jen Chen]], [[Shi-Jim Yen]] ('''2013'''). ''Apply Fuzzy Markup Language to Knowledge Representation for Game of Computer Go''. in [https://dblp.uni-trier.de/pers/hd/a/Acampora:Giovanni Giovanni Acampora], [https://dblp.uni-trier.de/pers/hd/l/Loia:Vincenzo Vincenzo Loia], [[Chang-Shing Lee]], [[Mei-Hui Wang]] (eds.) ('''2013'''). ''[https://link.springer.com/book/10.1007%2F978-3-642-35488-5 On the Power of Fuzzy Markup Language]''. [https://link.springer.com/bookseries/2941 Studies in Fuzziness and Soft Computing], Vol. 296, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer] <ref>[https://en.wikipedia.org/wiki/Fuzzy_markup_language Fuzzy markup language (FML) from Wikipedia]</ref>
 
'''2014'''
 
'''2014'''
 
* [[Lars Schaefers]] ('''2014'''). ''Parallel Monte-Carlo Tree Search for HPC Systems and its Application to Computer Go''. Ph.D. thesis, [[Paderborn University]], advisors [[Marco Platzner]], [[Ulf Lorenz]], [http://www.althofer.de/phd-thesis-schaefers.pdf pdf], [https://www.dropbox.com/s/x0lh7ky5lvj6c1y/PhdThesisSchaefers.pdf pdf]
 
* [[Lars Schaefers]] ('''2014'''). ''Parallel Monte-Carlo Tree Search for HPC Systems and its Application to Computer Go''. Ph.D. thesis, [[Paderborn University]], advisors [[Marco Platzner]], [[Ulf Lorenz]], [http://www.althofer.de/phd-thesis-schaefers.pdf pdf], [https://www.dropbox.com/s/x0lh7ky5lvj6c1y/PhdThesisSchaefers.pdf pdf]
Line 323: Line 334:
 
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Shi-Jim Yen]], [[Ting-Han Wei]], [[I-Chen Wu]], [[Ping-Chiang Chou]], [[Chun-Hsun Chou]], [[Ming-Wan Wang]], [[Tai-Hsiung Yang]] ('''2016'''). ''Human vs. Computer Go: Review and Prospect''. [https://arxiv.org/abs/1606.02032 arXiv:1606.02032]
 
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Shi-Jim Yen]], [[Ting-Han Wei]], [[I-Chen Wu]], [[Ping-Chiang Chou]], [[Chun-Hsun Chou]], [[Ming-Wan Wang]], [[Tai-Hsiung Yang]] ('''2016'''). ''Human vs. Computer Go: Review and Prospect''. [https://arxiv.org/abs/1606.02032 arXiv:1606.02032]
 
* [[Jonathan Rosenthal]] ('''2016'''). ''[https://www.research-collection.ethz.ch/handle/20.500.11850/156354 Deep Learning for Go]''. B.Sc. thesis,  [[ETH Zurich]]
 
* [[Jonathan Rosenthal]] ('''2016'''). ''[https://www.research-collection.ethz.ch/handle/20.500.11850/156354 Deep Learning for Go]''. B.Sc. thesis,  [[ETH Zurich]]
 +
* [[Kokolo Ikeda]], [[Simon Viennot]], [[Naoyuki Sato]] ('''2016'''). ''Detection and labeling of bad moves for coaching go''. [https://dblp.uni-trier.de/db/conf/cig/cig2016.html CIG 2016]
 
* [[Chenjun Xiao]], [[Martin Müller]] ('''2016'''). ''Factorization Ranking Model for Move Prediction in the Game of Go''. [[Conferences#AAAI-2016|AAAI 2016]]
 
* [[Chenjun Xiao]], [[Martin Müller]] ('''2016'''). ''Factorization Ranking Model for Move Prediction in the Game of Go''. [[Conferences#AAAI-2016|AAAI 2016]]
 
'''2017'''
 
'''2017'''
Line 330: Line 342:
 
* [[Thomas Wolf]] ('''2017'''). ''Seki with 2 Liberties per Chain in the Game of Go''. [[ICGA Journal#39_2|ICGA Journal, Vol. 39, No. 2]]
 
* [[Thomas Wolf]] ('''2017'''). ''Seki with 2 Liberties per Chain in the Game of Go''. [[ICGA Journal#39_2|ICGA Journal, Vol. 39, No. 2]]
 
* [[Jon Diamond]] ('''2017'''). ''A History of Go-playing Programs''. [[ICGA Journal#39_2|ICGA Journal, Vol. 39, No. 2]]
 
* [[Jon Diamond]] ('''2017'''). ''A History of Go-playing Programs''. [[ICGA Journal#39_2|ICGA Journal, Vol. 39, No. 2]]
 +
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Chia-Hsiu Kao]], [[Sheng-Chi Yang]], [[Yusuke Nojima]], [[Ryosuke Saga]], [[Nan Shuo]], [[Naoyuki Kubota]] ('''2017'''). ''FML-based Prediction Agent and Its Application to Game of Go''. [https://arxiv.org/abs/1704.04719 arXiv:1704.04719]
 +
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Sheng-Chi Yang]], [[Pi-Hsia Hung]], [[Su-Wei Lin]], [[Nan Shuo]], [[Naoyuki Kubota]], [[Chun-Hsun Chou]], [[Ping-Chiang Chou]], [[Chia-Hsiu Kao]] ('''2017'''). ''FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go''. [https://arxiv.org/abs/1707.04828 arXiv:1707.04828]
 
* [[David Silver]], [[Julian Schrittwieser]], [[Karen Simonyan]], [[Ioannis Antonoglou]], [[Shih-Chieh Huang|Aja Huang]], [[Arthur Guez]], [[Thomas Hubert]], [[Lucas Baker]], [[Matthew Lai]], [[Adrian Bolton]], [[Yutian Chen]], [[Timothy Lillicrap]], [[Fan Hui]], [[Laurent Sifre]], [[George van den Driessche]], [[Thore Graepel]], [[Demis Hassabis]] ('''2017'''). ''[https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html Mastering the game of Go without human knowledge]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 550, [https://www.gwern.net/docs/rl/2017-silver.pdf pdf]  
 
* [[David Silver]], [[Julian Schrittwieser]], [[Karen Simonyan]], [[Ioannis Antonoglou]], [[Shih-Chieh Huang|Aja Huang]], [[Arthur Guez]], [[Thomas Hubert]], [[Lucas Baker]], [[Matthew Lai]], [[Adrian Bolton]], [[Yutian Chen]], [[Timothy Lillicrap]], [[Fan Hui]], [[Laurent Sifre]], [[George van den Driessche]], [[Thore Graepel]], [[Demis Hassabis]] ('''2017'''). ''[https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html Mastering the game of Go without human knowledge]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 550, [https://www.gwern.net/docs/rl/2017-silver.pdf pdf]  
 
* [[David Silver]], [[Thomas Hubert]], [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Matthew Lai]], [[Arthur Guez]], [[Marc Lanctot]], [[Laurent Sifre]], [[Dharshan Kumaran]], [[Thore Graepel]], [[Timothy Lillicrap]], [[Karen Simonyan]], [[Demis Hassabis]] ('''2017'''). ''Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm''. [https://arxiv.org/abs/1712.01815 arXiv:1712.01815] » [[AlphaZero]]
 
* [[David Silver]], [[Thomas Hubert]], [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Matthew Lai]], [[Arthur Guez]], [[Marc Lanctot]], [[Laurent Sifre]], [[Dharshan Kumaran]], [[Thore Graepel]], [[Timothy Lillicrap]], [[Karen Simonyan]], [[Demis Hassabis]] ('''2017'''). ''Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm''. [https://arxiv.org/abs/1712.01815 arXiv:1712.01815] » [[AlphaZero]]
 
'''2018'''
 
'''2018'''
 +
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Li-Wei Ko]], [[Naoyuki Kubota]], [[Lu-An Lin]], [[Shinya Kitaoka]], [[Yu-Te Wang]], [[Shun-Feng Su]] ('''2018'''). ''Human and Smart Machine Co-Learning with Brain Computer Interface''. [https://arxiv.org/abs/1802.06521 arXiv:1802.06521]
 
* [[David Silver]], [[Thomas Hubert]], [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Matthew Lai]], [[Arthur Guez]], [[Marc Lanctot]], [[Laurent Sifre]], [[Dharshan Kumaran]], [[Thore Graepel]], [[Timothy Lillicrap]], [[Karen Simonyan]], [[Demis Hassabis]] ('''2018'''). ''[http://science.sciencemag.org/content/362/6419/1140 A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play]''. [https://en.wikipedia.org/wiki/Science_(journal) Science], Vol. 362, No. 6419 <ref>[https://deepmind.com/blog/alphazero-shedding-new-light-grand-games-chess-shogi-and-go/ AlphaZero: Shedding new light on the grand games of chess, shogi and Go] by [[David Silver]], [[Thomas Hubert]], [[Julian Schrittwieser]] and [[Demis Hassabis]], [[DeepMind]], December 03, 2018</ref>
 
* [[David Silver]], [[Thomas Hubert]], [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Matthew Lai]], [[Arthur Guez]], [[Marc Lanctot]], [[Laurent Sifre]], [[Dharshan Kumaran]], [[Thore Graepel]], [[Timothy Lillicrap]], [[Karen Simonyan]], [[Demis Hassabis]] ('''2018'''). ''[http://science.sciencemag.org/content/362/6419/1140 A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play]''. [https://en.wikipedia.org/wiki/Science_(journal) Science], Vol. 362, No. 6419 <ref>[https://deepmind.com/blog/alphazero-shedding-new-light-grand-games-chess-shogi-and-go/ AlphaZero: Shedding new light on the grand games of chess, shogi and Go] by [[David Silver]], [[Thomas Hubert]], [[Julian Schrittwieser]] and [[Demis Hassabis]], [[DeepMind]], December 03, 2018</ref>
 +
* [[Yuuto Kosaka]], [[Takeshi Ito]] ('''2018'''). ''Examination of Indicators for Estimating Players’ Strength by using Computer Go''. [[TAAI 2018]]
 +
* [[Chung-Chin Shih]], [[An-Jen Liu]], [[I-Chen Wu]] ('''2018'''). ''2017 “CITIC Securities Cup” – The 1st World AI Go Open''. [[ICGA Journal#40_4|ICGA Journal, Vol. 40, No. 4]]
 
'''2019'''
 
'''2019'''
* [[Yuandong Tian]], [[Jerry Ma]], [[Qucheng Gong]], [[Shubho Sengupta]], [[Zhuoyuan Chen]], [[James Pinkerton]], [[C. Lawrence Zitnick]] ('''2019'''). ''ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero''- [https://arxiv.org/abs/1902.04522 arXiv:1902.04522] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=69895 ELF OpenGo: An Open Reimplementation of AlphaZero] by Carl Lumma, [[CCC]], February 13, 2019</ref> <ref>[https://github.com/pytorch/ELF GitHub - pytorch/ELF: ELF: a platform for game research with AlphaGoZero/AlphaZero reimplementation]</ref>  
+
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Li-Wei Ko]], [[Bo-Yu Tsai]], [[Yi-Lin Tsai]], [[Sheng-Chi Yang]], [[Lu-An Lin]], [[Yi-Hsiu Lee]], [[Hirofumi Ohashi]], [[Naoyuki Kubota]], [[Nan Shuo]] ('''2019'''). ''PFML-based Semantic BCI Agent for Game of Go Learning and Prediction''. [https://arxiv.org/abs/1901.02999 arXiv:1901.02999]
 +
* [[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Li-Chuang Chen]], [[Yusuke Nojima]], [[Tzong-Xiang Huang]], [[Jinseok Woo]], [[Naoyuki Kubota]], [[Eri Sato-Shimokawara]], [[Toru Yamaguchi]] ('''2019'''). ''A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go''. [http://export.arxiv.org/abs/1901.07191 arXiv:1901.07191]
 +
* [[Yuandong Tian]], [[Jerry Ma]], [[Qucheng Gong]], [[Shubho Sengupta]], [[Zhuoyuan Chen]], [[James Pinkerton]], [[C. Lawrence Zitnick]] ('''2019'''). ''ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero''. [https://arxiv.org/abs/1902.04522 arXiv:1902.04522] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=69895 ELF OpenGo: An Open Reimplementation of AlphaZero] by Carl Lumma, [[CCC]], February 13, 2019</ref> <ref>[https://github.com/pytorch/ELF GitHub - pytorch/ELF: ELF: a platform for game research with AlphaGoZero/AlphaZero reimplementation]</ref>  
 +
* [[David J. Wu]] ('''2019'''). ''Accelerating Self-Play Learning in Go''. [https://arxiv.org/abs/1902.10565 arXiv:1902.10565] <ref>[https://groups.google.com/g/lczero/c/gecAk5DflmE/m/lUGWpjZXBwAJ KataGo] by [[Warren D. Smith]], [[Computer Chess Forums|LCZero Forum]], March 16, 2021</ref>
 +
* [[Yusaku Mandai]], [[Tomoyuki Kaneko]] ('''2019'''). ''RankNet for evaluation functions of the game of Go''. [[ICGA Journal#41_2|ICGA Journal, Vol. 41, No. 2]]
 +
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2019'''). ''Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model''. [https://arxiv.org/abs/1911.08265 arXiv:1911.08265] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=72381 New DeepMind paper] by GregNeto, [[CCC]], November 21, 2019</ref>
 +
* [[Hsiao-Chung Hsieh]], [[Ti-Rong Wu]], [[Ting-Han Wei]], [[I-Chen Wu]] ('''2019'''). ''Net2Net Extension for the AlphaGo Zero Algorithm''. [[Advances in Computer Games 16]]
 +
==2020 ...==
 +
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2020'''). ''[https://www.nature.com/articles/s41586-020-03051-4 Mastering Atari, Go, chess and shogi by planning with a learned model]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 588 <ref>[https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules?fbclid=IwAR3mSwrn1YXDKr9uuGm2GlFKh76wBilex7f8QvBiQecwiVmAvD6Bkyjx-rE MuZero: Mastering Go, chess, shogi and Atari without rules]</ref>
 +
* [[Tristan Cazenave]], [[Julien Sentuc]], [[Mathurin Videau]] ('''2021'''). ''Cosine Annealing, Mixnet and Swish Activation for Computer Go''. [[Advances in Computer Games 17]]
  
 
=Forum Posts=
 
=Forum Posts=
Line 372: Line 398:
 
* [https://groups.google.com/d/msg/computer-go-archive/0lbCSpxozos/vUsnILL3BwAJ mini-max with Policy and Value network] by [[Hiroshi Yamashita]], [https://groups.google.com/forum/#!forum/computer-go-archive Computer Go Archive], May 20, 2017
 
* [https://groups.google.com/d/msg/computer-go-archive/0lbCSpxozos/vUsnILL3BwAJ mini-max with Policy and Value network] by [[Hiroshi Yamashita]], [https://groups.google.com/forum/#!forum/computer-go-archive Computer Go Archive], May 20, 2017
 
* <span id="ReAlphaGo"></span>[http://www.talkchess.com/forum/viewtopic.php?t=60394&start=11 Re: World #1 Go Player Ke Jie accepts Google Alpha Go Match.] by [[Kai Laskos]], [[CCC]], May 22, 2017  » [[Go#TheFutureofGoSummit|The Future of Go Summit]]
 
* <span id="ReAlphaGo"></span>[http://www.talkchess.com/forum/viewtopic.php?t=60394&start=11 Re: World #1 Go Player Ke Jie accepts Google Alpha Go Match.] by [[Kai Laskos]], [[CCC]], May 22, 2017  » [[Go#TheFutureofGoSummit|The Future of Go Summit]]
* [https://groups.google.com/d/msg/computer-go-archive/WImAk15gRN4/bhA7kSAnBgAJ Neural nets for Go - chain pooling?] by [[David Wu]], [https://groups.google.com/forum/#!forum/computer-go-archive Computer Go Archive], August 18, 2017
+
* [https://groups.google.com/d/msg/computer-go-archive/WImAk15gRN4/bhA7kSAnBgAJ Neural nets for Go - chain pooling?] by [[David J. Wu|David Wu]], [https://groups.google.com/forum/#!forum/computer-go-archive Computer Go Archive], August 18, 2017
 
* [http://www.talkchess.com/forum/viewtopic.php?t=65481 We are doomed - AlphaGo Zero, learning only from basic rules] by [[Vincent Lejeune]], [[CCC]], October 18, 2017
 
* [http://www.talkchess.com/forum/viewtopic.php?t=65481 We are doomed - AlphaGo Zero, learning only from basic rules] by [[Vincent Lejeune]], [[CCC]], October 18, 2017
 
* [http://www.talkchess.com/forum/viewtopic.php?t=65484 AlphaGo Zero] by [[Alberto Sanjuan]], [[CCC]], October 19, 2017
 
* [http://www.talkchess.com/forum/viewtopic.php?t=65484 AlphaGo Zero] by [[Alberto Sanjuan]], [[CCC]], October 19, 2017
Line 383: Line 409:
 
* [https://groups.google.com/d/msg/computer-go-archive/xE30UZaOpZc/Y17gA0-zFwAJ GCP passing on the staff ...] by [[Ingo Althöfer]], [https://groups.google.com/forum/#!forum/computer-go-archive Computer Go Archive], January 28, 2019
 
* [https://groups.google.com/d/msg/computer-go-archive/xE30UZaOpZc/Y17gA0-zFwAJ GCP passing on the staff ...] by [[Ingo Althöfer]], [https://groups.google.com/forum/#!forum/computer-go-archive Computer Go Archive], January 28, 2019
 
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=69895 ELF OpenGo: An Open Reimplementation of AlphaZero] by Carl Lumma, [[CCC]], February 13, 2019
 
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=69895 ELF OpenGo: An Open Reimplementation of AlphaZero] by Carl Lumma, [[CCC]], February 13, 2019
 +
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=70899 Why are super top professional Go players so stupid?] by [[Kai Laskos]], [[CCC]], June 02, 2019
 +
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=72197 Some words on Leela Go Zero] by [[Kai Laskos]], [[CCC]], October 28, 2019 » [[Leela Zero]]
 +
==2020 ...==
 +
* [https://groups.google.com/g/lczero/c/gecAk5DflmE/m/lUGWpjZXBwAJ KataGo] by [[Warren D. Smith]], [[Computer Chess Forums|LCZero Forum]], March 16, 2021
  
 
=External Links=  
 
=External Links=  
Line 391: Line 421:
 
* [https://en.wikipedia.org/wiki/Go_and_mathematics Go and mathematics from Wikipedia]
 
* [https://en.wikipedia.org/wiki/Go_and_mathematics Go and mathematics from Wikipedia]
 
* [https://en.wikipedia.org/wiki/Ing_Chang-ki Ing Chang-ki from Wikipedia]
 
* [https://en.wikipedia.org/wiki/Ing_Chang-ki Ing Chang-ki from Wikipedia]
 +
* [https://www.youtube.com/watch?v=UkSOVnbC2Y8 2008-2016 Human vs. Computer Go], [https://en.wikipedia.org/wiki/YouTube YouTube] Video
 +
: {{#evu:https://www.youtube.com/watch?v=UkSOVnbC2Y8|alignment=left|valignment=top}}
 
==Tournaments==
 
==Tournaments==
 
* [http://www.smart-games.com/worldcompgo.html World Computer Go Championships]
 
* [http://www.smart-games.com/worldcompgo.html World Computer Go Championships]
Line 418: Line 450:
 
* [https://github.com/Tencent/PhoenixGo GitHub - Tencent/PhoenixGo: Go AI program which implements the AlphaGo Zero paper]
 
* [https://github.com/Tencent/PhoenixGo GitHub - Tencent/PhoenixGo: Go AI program which implements the AlphaGo Zero paper]
 
* [https://github.com/pytorch/ELF GitHub - pytorch/ELF: ELF: a platform for game research with AlphaGoZero/AlphaZero reimplementation] <ref>[[Yuandong Tian]], [[Jerry Ma]], [[Qucheng Gong]], [[Shubho Sengupta]], [[Zhuoyuan Chen]], [[James Pinkerton]], [[C. Lawrence Zitnick]] ('''2019'''). ''ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero''- [https://arxiv.org/abs/1902.04522 arXiv:1902.04522]</ref>
 
* [https://github.com/pytorch/ELF GitHub - pytorch/ELF: ELF: a platform for game research with AlphaGoZero/AlphaZero reimplementation] <ref>[[Yuandong Tian]], [[Jerry Ma]], [[Qucheng Gong]], [[Shubho Sengupta]], [[Zhuoyuan Chen]], [[James Pinkerton]], [[C. Lawrence Zitnick]] ('''2019'''). ''ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero''- [https://arxiv.org/abs/1902.04522 arXiv:1902.04522]</ref>
 +
* [https://github.com/lightvector/KataGo GitHub - lightvector/KataGo: GTP engine and self-play learning in Go]
 
==Misc==
 
==Misc==
 
* [http://www.win.tue.nl/~engels/go/variants.html Go Variants]
 
* [http://www.win.tue.nl/~engels/go/variants.html Go Variants]
Line 462: Line 495:
 
=References=
 
=References=
 
<references />
 
<references />
 
 
'''[[Games|Up one Level]]'''
 
'''[[Games|Up one Level]]'''
 +
[[Category:Videos]]
 +
[[Category:Quotes]]

Revision as of 15:45, 1 December 2021

Home * Games * Go

19*19 Go board [1]

The game of Go has attracted game researchers and programmers as an ambitious AI-challenge. Albert Zobrist was a pioneer, who wrote the first Go program in 1968 as part of his Ph.D. Thesis on pattern recognition [2]. Chess programmers, beside others, Rémi Coulom and Gian-Carlo Pascutto became successful Go programmers with their programs CrazyStone and Leela respectively. Competitive computer Go, as organized by the ICGA [3], is played on boards with 9x9 as well with default 19x19 grids. Since Go lacks a simple evaluation function mainly based on counting material, attempts to apply similar techniques and algorithms as in chess were less successful. The breakthrough in computer Go was accomplished by Monte-Carlo tree search and deep learning.

Progress

Monte-Carlo Go

After early trials to apply Monte Carlo methods to a Go playing program by Bernd Brügmann in 1993 [4], recent developments since the mid 2000s by Bruno Bouzy [5], and by Rémi Coulom, who coined the term Monte-Carlo Tree Search [6], in conjunction with UCT (Upper Confidence bounds applied to Trees) introduced by Levente Kocsis and Csaba Szepesvári [7], led to a breakthrough in computer Go [8].

CNNs

As mentioned by Ilya Sutskever and Vinod Nair in 2008 [9], convolutional neural networks are well suited for problems with a natural translation invariance, such as object recognition. Go has some translation invariance, because if all the pieces on a hypothetical Go board are shifted to the left, then the best move will also shift (with the exception of pieces that are on the boundary of the board). Many applications of neural networks to Go have already used convolutional neural networks, such as Nicol N. Schraudolph et al. [10], Erik van der Werf et al. [11], and Markus Enzenberger [12], among others.

In 2014, two teams independently investigated whether deep convolutional neural networks [13] could be used to directly represent and learn a move evaluation function for the game of Go. Christopher Clark and Amos Storkey trained an 8-layer convolutional neural network by supervised learning from a database of human professional games, which without any search, defeated the traditional search program Gnu Go in 86% of the games [14] [15] [16] [17] [18]. In their paper Move Evaluation in Go Using Deep Convolutional Neural Networks [19], Chris J. Maddison, Aja Huang, Ilya Sutskever, and David Silver report they trained a large 12-layer convolutional neural network in a similar way, to beat Gnu Go in 97% of the games, and matched the performance of a state-of-the-art Monte-Carlo Tree Search that simulates a million positions per move [20].

AlphaGo

In 2015, a team affiliated with Google DeepMind around David Silver, Aja Huang, Chris J. Maddison, and Demis Hassabis, supported by Google researchers John Nham and Ilya Sutskever, build a Go playing program dubbed AlphaGo, combining Monte-Carlo tree search with their 12-layer networks [21], the “policy network,” to select the next move, the “value network,” to predict the winner of the game. The neural networks were trained on 30 million moves from games played by human experts, until it could predict the human move 57 percent of the time. AlphaGo achieved a huge winning rate against other Go programs, and defeated European Go champion Fan Hui [22] in October 2015 with a 5 - 0 score [23] On March 9 to 15, 2016, AlphaGo won a $1M 5-game challenge match in Seoul versus Lee Sedol with 4 - 1 [24] [25] [26]. During The Future of Go Summit from May 23 to 27, 2017 in Wuzhen, China, AlphaGo won a three-game match versus current world No. 1 ranking player Ke Ji. After the Summit, AlphaGo is now retired from competitive play while DeepMind continues AI research in other areas [27].

AlphaGo Zero & AlphaZero

However, in October 2017, AlphaGo Zero, an evolution of AlphaGo was introduced. While previous versions were initially trained on thousands of human amateur and professional games to learn how to play Go, AlphaGo Zero learns exclusively by playing games against itself, starting from completely random play, to quickly surpass human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0 [28] [29]. AlphaGo Zero was further improved and even generalized for other games now dubbed AlphaZero, as published in December 2017 [30].

Fine Art

Fine Art is a Go playing entity developed since 2016 under the patronage of the Chinese media company Tencent by a team around Liu Yongsheng, along with Ma Bo, Tang Shanmin, Wu Guangyu, and Zhang Kaixu. It won the Computer Go UEC Cup at the University of Electro-Communications, Chōfu, Tokyo, Japan, in March 2017 against a field of 27 other programs including DeepZenGo and Crazy Stone [31]. In January 2018, it defeated Ke Jie 9P in 77 moves after giving two stones handicap [32] on Fox Weiqi [33] server [34].

Quotes

Quote by Gian-Carlo Pascutto in 2010 [35]:

There is no significant difference between an alpha-beta search with heavy LMR  and a static evaluator (current state of the art in chess) and an UCT searcher with a small exploration constant that does playouts (state of the art in go).
The shape of the tree they search is very similar. The main breakthrough in Go the last few years was how to backup an uncertain Monte Carlo score. This was solved. For chess this same problem was solved around the time quiescent search was developed.
Both are producing strong programs and we've proven for both the methods that they scale in strength as hardware speed goes up.
So I would say that we've successfully adopted the simple, brute force methods for chess to Go and they already work without increases in computer speed. The increases will make them progressively stronger though, and with further software tweaks they will eventually surpass humans. 

Computer Olympiads

See also

Search

Learning

Misc

Videos on Go

Selected Publications

[36]

1960 ...

1970 ...

1980 ...

1990

1991

1992

1993

1994

1995 ...

1996

1997

1998

1999

2000 ...

2001

2002

2003

2004

2005 ...

2006

2007

2008

2009

2010 ...

2011

2012

2013

2014

2015 ...

2016

2017

2018

2019

2020 ...

Forum Posts

2005 ...

Re: A thought about ratings by Don Dailey, Computer Go Archive, December 10, 2007
Re: A thought about ratings by Edward de Grijs, Computer Go Archive, December 10, 2007
Re: A thought about ratings by Don Dailey, Computer Go Archive, December 10, 2007

2010 ...

2015 ...

2017

2018

2019

2020 ...

External Links

Tournaments

Computer Go Archives

Program database - Program list
Program database - Programmer list
Computer Go - Past Events - Acorn 1984 » BBC Micro
1998 Ing Computer Goe Cup - Stories
Human-Computer Go Challenges

Computer Go Pages

Go Servers at Sensei's Library
Counting Legal Positions in Go by John Tromp, January 20, 2016

Open Source

Misc

Go Challenge

AlphaGo

The computer that mastered Go, with Demis Hassabis and David Silver, YouTube Video

Fine Art

References

  1. The Age of Intelligent Machines
  2. Albert Zobrist (1970). Feature Extraction and Representation for Pattern Recognition and the Game of Go. Ph.D. thesis , University of Wisconsin, also published as technical report, pdf
  3. Go at the Computer Olympiad
  4. Bernd Brügmann (1993). Monte Carlo Go. pdf
  5. Bruno Bouzy (2005). Associating domain-dependent knowledge and Monte Carlo approaches within a go program. Information Sciences, Heuristic Search and Computer Game Playing IV
  6. Rémi Coulom (2006). Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search. CG 2006, pdf
  7. Levente Kocsis, Csaba Szepesvári (2006). Bandit based Monte-Carlo Planning. ECML-06, LNCS/LNAI 4212, pdf
  8. Sylvain Gelly, Marc Schoenauer, Michèle Sebag, Olivier Teytaud, Levente Kocsis, David Silver, Csaba Szepesvári (2012). The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions. Communications of the ACM, Vol. 55, No. 3, pdf preprint
  9. Ilya Sutskever, Vinod Nair (2008). Mimicking Go Experts with Convolutional Neural Networks. ICANN 2008, pdf
  10. Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski (1993). Temporal Difference Learning of Position Evaluation in the Game of Go. NIPS 1993
  11. Erik van der Werf, Jos Uiterwijk, Eric Postma, Jaap van den Herik (2002). Local Move Prediction in Go. CG 2002
  12. Markus Enzenberger (2003). Evaluation in Go by a Neural Network using Soft Segmentation. Advances in Computer Games 10, pdf
  13. Convolutional neural network from Wikipedia
  14. Christopher Clark, Amos Storkey (2014). Teaching Deep Convolutional Neural Networks to Play Go. arXiv:1412.3409
  15. Deep learning for… Go by Erik Bernhardsson, December 11, 2014
  16. Teaching Deep Convolutional Neural Networks to Play Go by Hiroshi Yamashita, The Computer-go Archives, December 14, 2014
  17. Why Neural Networks Look Set to Thrash the Best Human Go Players for the First Time | MIT Technology Review, December 15, 2014
  18. Teaching Deep Convolutional Neural Networks to Play Go by Michel Van den Bergh, CCC, December 16, 2014
  19. Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver (2014). Move Evaluation in Go Using Deep Convolutional Neural Networks. arXiv:1412.6564v1
  20. Move Evaluation in Go Using Deep Convolutional Neural Networks by Aja Huang, The Computer-go Archives, December 19, 2014
  21. David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis (2016). Mastering the game of Go with deep neural networks and tree search. Nature, Vol. 529
  22. Fan Hui at Sensei's Library
  23. Game Over? AlphaGo Beats Pro 5-0 in Major AI Advance « American Go E-Journal, January 27, 2016
  24. DeepMind - YouTube Channel
  25. Video Interview with Rémi Coulom on AlphaGo, February 2016
  26. Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol, BBC News, March 12, 2016
  27. AlphaGo’s Designers Explore New AI After Winning Big in China by Cade Metz, Wired, May 27, 2017
  28. AlphaGo Zero: Learning from scratch by Demis Hassabis and David Silver, DeepMind, October 18, 2017
  29. David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis (2017). Mastering the game of Go without human knowledge. Nature, Vol. 550
  30. David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis (2017). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. arXiv:1712.01815
  31. Fine Art (software) from Wikipedia
  32. Two stones! Fine Art defeated Ke Jie 9P after giving two stones handicap. – Website of The International Go Federation, January 19, 2018
  33. Go Servers at Sensei's Library - Fox Weiqi
  34. Breakthrough: Fine Art beating Ke Jie with 2 Handicap Stones by Ingo Althöfer, Computer Go Archive, January 20, 2018
  35. Re: Chess vs Go // AI vs IA by Gian-Carlo Pascutto, June 02, 2010
  36. Computer Go Bibliography, University of Alberta
  37. GoTools - TsumeGo Solving Software
  38. Gobble
  39. Nici Schraudolph’s go networks, review by Jay Scott
  40. Mathematical Go from Sensei's Library
  41. EZ-GO at Sensei's Library
  42. Tsumego at Sensei's Library
  43. steganography from Wikipedia
  44. The Shodan Go Bet
  45. Re: Teaching Deep Convolutional Neural Networks to Play Go by Erik van der Werf, The Computer-go Archives, December 15, 2014
  46. Capturing race from Wikipedia
  47. Fuzzy markup language (FML) from Wikipedia
  48. Franz-Josef Dickhut from Wikipedia, Rémi Coulom
  49. codecentric go challenge 2014: Interviews with Franz-Josef Dickhut and Rémi Coulom - codecentric Blog by Raymond Georg Snatzke, October 1, 2014
  50. codecentric go challenge 2014: Final Interviews - codecentric Blog by Raymond Georg Snatzke, November 27, 2014 (German)
  51. How Facebook’s AI Researchers Built a Game-Changing Go Engine | MIT Technology Review, December 04, 2015
  52. Combining Neural Networks and Search techniques (GO) by Michael Babigian, CCC, December 08, 2015
  53. Re: Minmax backup operator for MCTS by Brahim Hamadicharef, CCC, December 30, 2017
  54. AlphaZero: Shedding new light on the grand games of chess, shogi and Go by David Silver, Thomas Hubert, Julian Schrittwieser and Demis Hassabis, DeepMind, December 03, 2018
  55. ELF OpenGo: An Open Reimplementation of AlphaZero by Carl Lumma, CCC, February 13, 2019
  56. GitHub - pytorch/ELF: ELF: a platform for game research with AlphaGoZero/AlphaZero reimplementation
  57. KataGo by Warren D. Smith, LCZero Forum, March 16, 2021
  58. New DeepMind paper by GregNeto, CCC, November 21, 2019
  59. MuZero: Mastering Go, chess, shogi and Atari without rules
  60. The Mystery of Go, the Ancient Game That Computers Still Can’t Win by Alan Levinovitz, Wired, May 12, 2014
  61. Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, C. Lawrence Zitnick (2019). ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero- arXiv:1902.04522
  62. Wired Article on Computer GO by Edmund Moshammer, CCC, May 13, 2014
  63. World #1 Go Player Ke Jie accepts Google Alpha Go Match.. by AA Ross, CCC, June 07, 2016
  64. Ke Jie from Wikipedia

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