Difference between revisions of "Bonanza"
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'''Bonanza''', (Bonanza Feliz)<br/> | '''Bonanza''', (Bonanza Feliz)<br/> | ||
− | + | an [[XBoard]] compliant [[:Category:Open Source|open source]] [[Shogi]] engine developed by primary author [[Kunihito Hoki]], started in 2004, at times supported by [[Takuya Obata]], [[Takuya Sugiyama]], and [[Takeshi Ito]] | |
<ref>[https://groups.google.com/d/msg/shogi-l/bazz1reADOY/KB8UvBZQf_cJ WCSC20 Participant List] by [[Takenobu Takizawa|Nobu]], [[Computer Chess Forums|SHOGI-L]], February 02, 2010</ref>. | <ref>[https://groups.google.com/d/msg/shogi-l/bazz1reADOY/KB8UvBZQf_cJ WCSC20 Participant List] by [[Takenobu Takizawa|Nobu]], [[Computer Chess Forums|SHOGI-L]], February 02, 2010</ref>. | ||
Bonanza is top contender at [[Computer Olympiad|Computer Olympiads]] and [[World Computer Shogi Championship|World Computer Shogi Championships]] | Bonanza is top contender at [[Computer Olympiad|Computer Olympiads]] and [[World Computer Shogi Championship|World Computer Shogi Championships]] | ||
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a [[Supervised Learning|supervised]] [[Automated Tuning|tuning method]] based on [[Automated Tuning#MoveAdaption|move adaptation]], | a [[Supervised Learning|supervised]] [[Automated Tuning|tuning method]] based on [[Automated Tuning#MoveAdaption|move adaptation]], | ||
dubbed the '''Bonanza Method''' which evolved to [[Minimax Tree Optimization]] (MMTO). | dubbed the '''Bonanza Method''' which evolved to [[Minimax Tree Optimization]] (MMTO). | ||
− | Bonanza utilizes and tuned [[Piece-Square Tables|piece-square tables]] indexed by king location and further two-piece locations, dubbed KPP or | + | Bonanza utilizes and tuned [[Piece-Square Tables|piece-square tables]] indexed by king location and further two-piece locations and side to move (turn), dubbed '''KPP''', '''KKP''' or '''KPPT''', which was used in many other Shogi programs <ref>[https://groups.google.com/d/msg/shogi-l/c4-dY44P8Mw/M3z-RtFR-tsJ The 25th World Computer Shogi Championships] by [[Reijer Grimbergen]] on behalf of [[Takenobu Takizawa]], [[Computer Chess Forums|SHOGI-L]], February 11, 2015</ref>, and has influenced the design of [[NNUE]] <ref>[http://yaneuraou.yaneu.com/2020/05/03/%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92%E3%82%A8%E3%83%B3%E3%82%B8%E3%83%8B%E3%82%A2%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E5%B0%86%E6%A3%8Bai%E9%96%8B%E7%99%BA%E5%85%A5%E9%96%80%E3%81%9D%E3%81%AE1/ 機械学習エンジニアのための将棋AI開発入門その1 Introduction to Shogi AI development for machine learning engineers Part 1], May 03, 2020 (Japanese)</ref>. |
− | which was used in many other Shogi programs <ref>[https://groups.google.com/d/msg/shogi-l/c4-dY44P8Mw/M3z-RtFR-tsJ The 25th World Computer Shogi Championships] by [[Reijer Grimbergen]] on behalf of [[Takenobu Takizawa]], [[Computer Chess Forums|SHOGI-L]], February 11, 2015</ref>, and has influenced the design of [[NNUE]] <ref>[http://yaneuraou.yaneu.com/2020/05/03/%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92%E3%82%A8%E3%83%B3%E3%82%B8%E3%83%8B%E3%82%A2%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E5%B0%86%E6%A3%8Bai%E9%96%8B%E7%99%BA%E5%85%A5%E9%96%80%E3%81%9D%E3%81%AE1/ 機械学習エンジニアのための将棋AI開発入門その1 Introduction to Shogi AI development for machine learning engineers Part 1], May 03, 2020 (Japanese)</ref>. | ||
Beside a [[Parallel Search|parallel tree search]], Bonanza is able to apply parallelization by the so called '''Consensus''' method | Beside a [[Parallel Search|parallel tree search]], Bonanza is able to apply parallelization by the so called '''Consensus''' method |
Latest revision as of 22:33, 27 February 2021
Bonanza, (Bonanza Feliz)
an XBoard compliant open source Shogi engine developed by primary author Kunihito Hoki, started in 2004, at times supported by Takuya Obata, Takuya Sugiyama, and Takeshi Ito
[2].
Bonanza is top contender at Computer Olympiads and World Computer Shogi Championships
and so far two times champion, winning the WCSC16 in 2006, and the WCSC23 in 2013 [3].
Contents
Description
Bonanza is written in C and utilizes 9x9 Bitboards in form of three 32-bit unsigned integers. It is a fractional ply alpha-beta engine performing a principal variation search with transposition table, null move pruning, recursive iterative-deepening for PV-nodes, various extensions, reductions, and futility pruning. Bonanza pioneered in large-scale machine learning of static evaluation functions, a supervised tuning method based on move adaptation, dubbed the Bonanza Method which evolved to Minimax Tree Optimization (MMTO). Bonanza utilizes and tuned piece-square tables indexed by king location and further two-piece locations and side to move (turn), dubbed KPP, KKP or KPPT, which was used in many other Shogi programs [4], and has influenced the design of NNUE [5].
Beside a parallel tree search, Bonanza is able to apply parallelization by the so called Consensus method [6], a kind of triple-brain approach where multiple, slightly modified instances of the same engine vote for the best move [7] [8].
Publications
- Kunihito Hoki (2006). Optimal control of minimax search result to learn positional evaluation. 11th Game Programming Workshop (Japanese)
- Takuya Obata, Takuya Sugiyama, Kunihito Hoki, Takeshi Ito (2010). Consultation Algorithm for Computer Shogi: Move Decisions by Majority. CG 2010
- Takuya Sugiyama, Takuya Obata, Kunihito Hoki, Takeshi Ito (2010). Optimistic Selection Rule Better Than Majority Voting System. CG 2010
- Tomoyuki Kaneko, Kunihito Hoki (2011). Analysis of Evaluation-Function Learning by Comparison of Sibling Nodes. Advances in Computer Games 13
- Kunihito Hoki, Tomoyuki Kaneko (2011). The Global Landscape of Objective Functions for the Optimization of Shogi Piece Values with a Game-Tree Search. Advances in Computer Games 13
- Kunihito Hoki, Masakazu Muramatsu (2012). Efficiency of three Forward-Pruning Techniques in Shogi: Futility Pruning, Null-move Pruning, and Late Move Reduction (LMR). Entertainment Computing, Vol. 3, No. 3
- Kunihito Hoki, Tomoyuki Kaneko (2014). Large-Scale Optimization for Evaluation Functions with Minimax Search. JAIR Vol. 49, pdf [9]
- Kunihito Hoki, Seiya Omori, Takeshi Ito (2014). Analysis of Performance of Consultation Methods in Computer Chess. Journal of Information Science and Engineering, Vol. 30, pdf
- Takenobu Takizawa, Takeshi Ito, Takuya Hiraoka, Kunihito Hoki (2015). Contemporary Computer Shogi. Encyclopedia of Computer Graphics and Games
Forum Posts
- Source file of Bonanza 4.0.3 became available to downloadable by Takodori, SHOGI-L, January 29, 2009
- XBoard version of Bonanza by Harm Geert Muller, CCC, October 20, 2010
- The Japan Times on-line 20111102 and Shogi by Alain Van hentenryck, SHOGI-L, November 02, 2011
- Bonanza wins Computer Shogi Championship by Hiroshi Yamashita, SHOGI-L, May 06, 2013
- Bonanza version adapted for interactive analysis (release) by Harm Geert Muller, CCC, September 14, 2013
- Bonanza version adapted for interactive analysis by Harm Geert Muller, SHOGI-L, September 14, 2013
- better appearance of BONANZA by shogipl..., SHOGI-L, October 21, 2014
External Links
Shogi Engine
- GitHub - ianfab/bonanza: strong shogi engine (with support for XBoard protocol) hosted by Fabian Fichter
- Bonanza Shogi Engine hosted by Harm Geert Muller
- Bonanza's ICGA Tournaments
- Famous Shogi Games: Bonanza vs Watanabe (March 21, 2007), YouTube Video
Misc
- bonanza - Wiktionary
- Bonanza (disambiguation) from Wikipedia
- Bonanza from Wikipedia
- Feliz from Wikipedia
References
- ↑ Photo of the main cast of Bonanza. From left - Dan Blocker (Hoss), Michael Landon (Little Joe), Lorne Greene (Ben), Pernell Roberts (Adam), September 20, 1959, Author: NBC Television. Category:Bonanza (TV series) - Wikimedia Commons
- ↑ WCSC20 Participant List by Nobu, SHOGI-L, February 02, 2010
- ↑ Takenobu Takizawa, Takeshi Ito, Takuya Hiraoka, Kunihito Hoki (2015). Contemporary Computer Shogi. Encyclopedia of Computer Graphics and Games
- ↑ The 25th World Computer Shogi Championships by Reijer Grimbergen on behalf of Takenobu Takizawa, SHOGI-L, February 11, 2015
- ↑ 機械学習エンジニアのための将棋AI開発入門その1 Introduction to Shogi AI development for machine learning engineers Part 1, May 03, 2020 (Japanese)
- ↑ Bonanzaの特徴は、 Characteristics of Bonanza - wcsc23 2013
- ↑ Takuya Obata, Takuya Sugiyama, Kunihito Hoki, Takeshi Ito (2010). Consultation Algorithm for Computer Shogi: Move Decisions by Majority. CG 2010
- ↑ Kunihito Hoki, Seiya Omori, Takeshi Ito (2014). Analysis of Performance of Consultation Methods in Computer Chess. Journal of Information Science and Engineering, Vol. 30, pdf
- ↑ MMTO for evaluation learning by Jon Dart, CCC, January 25, 2015