圍棋系統(tǒng)的設(shè)計(jì) 含英文翻譯.doc
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圍棋系統(tǒng)的設(shè)計(jì) 含英文翻譯,摘要本文通過(guò)對(duì)幾個(gè)頂尖電腦圍棋程序的研究,從認(rèn)知科學(xué)的角度介紹了電腦圍棋,并特別針對(duì)電腦圍棋編程人員(或有意投身于此的程序員)說(shuō)明圍棋作為一 個(gè)認(rèn)知科學(xué)研究領(lǐng)域的日益增長(zhǎng)的重要性。對(duì)于手談、go4++、many faces of go、go intellect 和explorer等幾個(gè)目前最優(yōu)秀的電腦圍棋程序,我們概要...
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內(nèi)容介紹
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摘要
本文通過(guò)對(duì)幾個(gè)頂尖電腦圍棋程序的研究,從認(rèn)知科學(xué)的角度介紹了電腦圍棋,并特別針對(duì)電腦圍棋編程人員(或有意投身于此的程序員)說(shuō)明圍棋作為一 個(gè)認(rèn)知科學(xué)研究領(lǐng)域的日益增長(zhǎng)的重要性。對(duì)于手談、Go4++、Many Faces of Go、Go Intellect 和Explorer等幾個(gè)目前最優(yōu)秀的電腦圍棋程序,我們概要介紹了這些程序所涉及的人工智能技術(shù),不得不面對(duì)的關(guān)鍵性挑戰(zhàn)和博弈樹搜索所牽涉的問(wèn)題,由 此揭示在圍棋領(lǐng)域不能很好地移植計(jì)算機(jī)國(guó)際象棋技術(shù)的原因。
Abstract
Based on the number of top computer chess procedures from the perspective of cognitive science of computer chess. especially in computer chess programmers (or programmers who are interested in joining this) as one cognitive research shows Go the growing importance of the field. On hand for that Go4++, Many Faces of Go. Go Intellect Explorer and several of the most outstanding computer chess procedures We outline the procedures involved in artificial intelligence technology Game and had to face the critical challenges involved in the search tree. Go to reveal in this area by the transplant computer chess unable to technical reasons.
參考文獻(xiàn)
Allis, V.Searching for solutions in games and artificial intelligence. PhD thesis, University of Limburg, Maastricht, 1994.
Burmeister, J.&Wiles, J. The challenge of Go as a domain for AI research: a comparison between Go and chess. In proceedings of the Third Australian and New Zealand Conference on Intelligent Information System, pages 181-186, Perth, November 1995. IEEE Western Australia Section.
Chen, K. Group Identification in Computer Go. In D.N.L.Levy and B.F.Beal, (eds), Heuristic Programming in Aritificial Intelligence: the First Computer Olympiad, pages 195-210. Ellis Horwood, Chichester, 1989.
Chen, K. The move decision process of Go Intellect. In David Erbach, editor, Computer Go, 14: 9-17, 1990.
Chen, K. Attack and defence. In H. J. Van den Herik and L. V. Allis,(ed)s, Heuristic Programming in Artificial Intelligence 3 - The Third Computer Olympiad, pages 146-156. Ellis Horwood, Chichester, 1992.
Donnelly, P., Corr, P., and Crookes, D. Evolving Go playing strategy in neurl networks, 1994. Available on the Internet at ftp://igs.nuri.net/Go/comp/egpsnn.ps.z.
Fotland, D. Knowledge representation in The Many Faces of Go,1993. Available on the Internet at ftp://igs.nuri.net/Go/comp/mfg.z.
Lishtenstein, D. & Sipser, M. Go is polynomial-space hard. Journal of the ACM, 27(2):393-401, 1980.
Muller, M. Computer Go as a sum of local games: an application of combinatorial game theory. PhD thesis, Swiss Federal Institute of Technology Zurich, 1995.
Pell, B. Exploratory learning in the game of Go. In D. N. L. Levy and D.F. F. Beal,(eds), Heuristic Programming in Artificial Intelligence 2 - The Second Computer Olympiad, volume 2. Ellis Horwood, 1991
Robson, J. The complexity of Go. In R. E. A. Mason, (ed), Proceedings of the IFIP 9th World Computer Congress, pages 413-417, North Holland, 1983. IFIP, Elsevier Science Publishers.
Ryder, J. Heuristic analysis of large trees as generated in the game of Go. Phd thesis, Department of Computer Science, Standford University, 1971.
Saito, Y. & Yoshikawa, A. Go as a testbed for Cognitive Science studies. In IJCAI Workshop Proceedings Using Games as an Experimental Testbed for AI Research, 1997.
Schraudolph, N., Dayan, P. and Sejnowski, T. Temporal difference learning of position eva luation in the game of Go. In J. D. Cowan, G. Tesauro and J. Alspector, (eds), Advances in Neural Information Processing 6, pages 817-824. Morgan Kaufmann, San Francisco, 1994.
Zorbrist, A. Amodel of visual organization for game Go. In Proceedings of the Spring Joint Computer Conference, 34: 103-112, 1969.
Zorbrist, A. Feature extractions and representation for pattern recognition and the game of Go. PhD thesis, Graduate School of the University of Wisconsin, 1970.
References
Allis. V.Searching for solutions in games and artific ial intelligence. PhD thesis, University of Limburg, Maastricht, 1994.
Burmeister, J.&Wiles. J. The challenge of Go as a domain for AI research : a comparison between chess and Go. In proceed ings of the Third Australian and New Zealand Con ference on Intelligent Information System , pages 181-186, Perth , November 1995. IEEE Western Australia Secti on.
Chen, K. Group Identification in Computer Go. In D.N. L.Levy and B.F.Beal, (eds). Heuristic Programming in Aritificial Intelli Contemporary : the First Computer Olympiad. pages 195-210. Ellis Horwood, Chichester, 1989.
Chen, K. The move decision process of Go Intellect. In David Erbach, editor, Computer Go, 14 : 9-17, 1990.
Chen, K. Attack and defense. In H. J. Van den Herik and L. V. Allis, (ed.) s, Heuristic Programming in Artificial Intellig in healthy 3 - The Third Computer Olympiad. pages 146-156. Ellis Horwood, Chichester, 1992.
Donnelly, P. , Corr, P. , And Crookes. D. Evolving Go playing strategy in neurl ne..
本文通過(guò)對(duì)幾個(gè)頂尖電腦圍棋程序的研究,從認(rèn)知科學(xué)的角度介紹了電腦圍棋,并特別針對(duì)電腦圍棋編程人員(或有意投身于此的程序員)說(shuō)明圍棋作為一 個(gè)認(rèn)知科學(xué)研究領(lǐng)域的日益增長(zhǎng)的重要性。對(duì)于手談、Go4++、Many Faces of Go、Go Intellect 和Explorer等幾個(gè)目前最優(yōu)秀的電腦圍棋程序,我們概要介紹了這些程序所涉及的人工智能技術(shù),不得不面對(duì)的關(guān)鍵性挑戰(zhàn)和博弈樹搜索所牽涉的問(wèn)題,由 此揭示在圍棋領(lǐng)域不能很好地移植計(jì)算機(jī)國(guó)際象棋技術(shù)的原因。
Abstract
Based on the number of top computer chess procedures from the perspective of cognitive science of computer chess. especially in computer chess programmers (or programmers who are interested in joining this) as one cognitive research shows Go the growing importance of the field. On hand for that Go4++, Many Faces of Go. Go Intellect Explorer and several of the most outstanding computer chess procedures We outline the procedures involved in artificial intelligence technology Game and had to face the critical challenges involved in the search tree. Go to reveal in this area by the transplant computer chess unable to technical reasons.
參考文獻(xiàn)
Allis, V.Searching for solutions in games and artificial intelligence. PhD thesis, University of Limburg, Maastricht, 1994.
Burmeister, J.&Wiles, J. The challenge of Go as a domain for AI research: a comparison between Go and chess. In proceedings of the Third Australian and New Zealand Conference on Intelligent Information System, pages 181-186, Perth, November 1995. IEEE Western Australia Section.
Chen, K. Group Identification in Computer Go. In D.N.L.Levy and B.F.Beal, (eds), Heuristic Programming in Aritificial Intelligence: the First Computer Olympiad, pages 195-210. Ellis Horwood, Chichester, 1989.
Chen, K. The move decision process of Go Intellect. In David Erbach, editor, Computer Go, 14: 9-17, 1990.
Chen, K. Attack and defence. In H. J. Van den Herik and L. V. Allis,(ed)s, Heuristic Programming in Artificial Intelligence 3 - The Third Computer Olympiad, pages 146-156. Ellis Horwood, Chichester, 1992.
Donnelly, P., Corr, P., and Crookes, D. Evolving Go playing strategy in neurl networks, 1994. Available on the Internet at ftp://igs.nuri.net/Go/comp/egpsnn.ps.z.
Fotland, D. Knowledge representation in The Many Faces of Go,1993. Available on the Internet at ftp://igs.nuri.net/Go/comp/mfg.z.
Lishtenstein, D. & Sipser, M. Go is polynomial-space hard. Journal of the ACM, 27(2):393-401, 1980.
Muller, M. Computer Go as a sum of local games: an application of combinatorial game theory. PhD thesis, Swiss Federal Institute of Technology Zurich, 1995.
Pell, B. Exploratory learning in the game of Go. In D. N. L. Levy and D.F. F. Beal,(eds), Heuristic Programming in Artificial Intelligence 2 - The Second Computer Olympiad, volume 2. Ellis Horwood, 1991
Robson, J. The complexity of Go. In R. E. A. Mason, (ed), Proceedings of the IFIP 9th World Computer Congress, pages 413-417, North Holland, 1983. IFIP, Elsevier Science Publishers.
Ryder, J. Heuristic analysis of large trees as generated in the game of Go. Phd thesis, Department of Computer Science, Standford University, 1971.
Saito, Y. & Yoshikawa, A. Go as a testbed for Cognitive Science studies. In IJCAI Workshop Proceedings Using Games as an Experimental Testbed for AI Research, 1997.
Schraudolph, N., Dayan, P. and Sejnowski, T. Temporal difference learning of position eva luation in the game of Go. In J. D. Cowan, G. Tesauro and J. Alspector, (eds), Advances in Neural Information Processing 6, pages 817-824. Morgan Kaufmann, San Francisco, 1994.
Zorbrist, A. Amodel of visual organization for game Go. In Proceedings of the Spring Joint Computer Conference, 34: 103-112, 1969.
Zorbrist, A. Feature extractions and representation for pattern recognition and the game of Go. PhD thesis, Graduate School of the University of Wisconsin, 1970.
References
Allis. V.Searching for solutions in games and artific ial intelligence. PhD thesis, University of Limburg, Maastricht, 1994.
Burmeister, J.&Wiles. J. The challenge of Go as a domain for AI research : a comparison between chess and Go. In proceed ings of the Third Australian and New Zealand Con ference on Intelligent Information System , pages 181-186, Perth , November 1995. IEEE Western Australia Secti on.
Chen, K. Group Identification in Computer Go. In D.N. L.Levy and B.F.Beal, (eds). Heuristic Programming in Aritificial Intelli Contemporary : the First Computer Olympiad. pages 195-210. Ellis Horwood, Chichester, 1989.
Chen, K. The move decision process of Go Intellect. In David Erbach, editor, Computer Go, 14 : 9-17, 1990.
Chen, K. Attack and defense. In H. J. Van den Herik and L. V. Allis, (ed.) s, Heuristic Programming in Artificial Intellig in healthy 3 - The Third Computer Olympiad. pages 146-156. Ellis Horwood, Chichester, 1992.
Donnelly, P. , Corr, P. , And Crookes. D. Evolving Go playing strategy in neurl ne..