MOTIVATION in Re
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
The 12Th Top Chess Engine Championship
TCEC12: the 12th Top Chess Engine Championship Article Accepted Version Haworth, G. and Hernandez, N. (2019) TCEC12: the 12th Top Chess Engine Championship. ICGA Journal, 41 (1). pp. 24-30. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-190090 Available at http://centaur.reading.ac.uk/76985/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . To link to this article DOI: http://dx.doi.org/10.3233/ICG-190090 Publisher: The International Computer Games Association All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online TCEC12: the 12th Top Chess Engine Championship Guy Haworth and Nelson Hernandez1 Reading, UK and Maryland, USA After the successes of TCEC Season 11 (Haworth and Hernandez, 2018a; TCEC, 2018), the Top Chess Engine Championship moved straight on to Season 12, starting April 18th 2018 with the same divisional structure if somewhat evolved. Five divisions, each of eight engines, played two or more ‘DRR’ double round robin phases each, with promotions and relegations following. Classic tempi gradually lengthened and the Premier division’s top two engines played a 100-game match to determine the Grand Champion. The strategy for the selection of mandated openings was finessed from division to division. -
A New Look at the Tayler by David Kane
A New Look at the Tayler by David kane I: Introduction The Tayler Variation (aka the Tayler Opening) is a line that has been unjustly neglected, in my view. The line is of surprisingly recent vintage though it is often confused with the Inverted Hungarian (or Inverted Hanham Defense), a line which shares the same opening moves: 1. e4 e5 2. Nf3 Nc6 3. Be2: The Inverted Hungarian is an old opening, dating back to the 1860ʼs, at least. Tartakower played it a few times in the 1920ʼs with mixed results, using the continuation, 3...Nf6 4. d3: a rather unenterprising setup for White. In 1981 British player, John Tayler (see biographical note), published an article in the British publication Chess (vol. 46) on a line he had developed stemming from the sharp 4. d4!?. This is a move which apparently no one had thought to play before, and one that transforms the sedate Inverted Hungarian into something else altogether. Technically, it is really the Tayler Variation to the Inverted Hungarian Defense rather than the Tayler Opening, though through usage, the terms are interchangeable for all practical purposes. As has been so often the case when it comes to unorthodox lines, I first heard of this opening via Mike Basman when he published a cassette on it back in the early 80ʼs (still available through audiochess.com). Tayler 2 The line stirred some interest at the time but gradually seems to have been forgotten. The final nail in the coffin was probably some light analysis published by Eric Schiller in Gambit Chess Openings (and elsewhere) where he dismisses the line primarily due to his loss in the game Schiller-Martinovsky, Chicago 1986. -
Chess Engine Using Deep Reinforcement Learning Kamil Klosowski
Chess Engine Using Deep Reinforcement Learning Kamil Klosowski 916847 May 2019 Abstract Reinforcement learning is one of the most rapidly developing areas of Artificial Intelligence. The goal of this project is to analyse, implement and try to improve on AlphaZero architecture presented by Google DeepMind team. To achieve this I explore different architectures and methods of training neural networks as well as techniques used for development of chess engines. Project Dissertation submitted to Swansea University in Partial Fulfilment for the Degree of Bachelor of Science Department of Computer Science Swansea University Declaration This work has not previously been accepted in substance for any degree and is not being currently submitted for any degree. May 13, 2019 Signed: Statement 1 This dissertation is being submitted in partial fulfilment of the requirements for the degree of a BSc in Computer Science. May 13, 2019 Signed: Statement 2 This dissertation is the result of my own independent work/investigation, except where otherwise stated. Other sources are specifically acknowledged by clear cross referencing to author, work, and pages using the bibliography/references. I understand that fail- ure to do this amounts to plagiarism and will be considered grounds for failure of this dissertation and the degree examination as a whole. May 13, 2019 Signed: Statement 3 I hereby give consent for my dissertation to be available for photocopying and for inter- library loan, and for the title and summary to be made available to outside organisations. May 13, 2019 Signed: 1 Acknowledgment I would like to express my sincere gratitude to Dr Benjamin Mora for supervising this project and his respect and understanding of my preference for largely unsupervised work. -
Learning Long-Term Chess Strategies from Databases
Mach Learn (2006) 63:329–340 DOI 10.1007/s10994-006-6747-7 TECHNICAL NOTE Learning long-term chess strategies from databases Aleksander Sadikov · Ivan Bratko Received: March 10, 2005 / Revised: December 14, 2005 / Accepted: December 21, 2005 / Published online: 28 March 2006 Springer Science + Business Media, LLC 2006 Abstract We propose an approach to the learning of long-term plans for playing chess endgames. We assume that a computer-generated database for an endgame is available, such as the king and rook vs. king, or king and queen vs. king and rook endgame. For each position in the endgame, the database gives the “value” of the position in terms of the minimum number of moves needed by the stronger side to win given that both sides play optimally. We propose a method for automatically dividing the endgame into stages characterised by different objectives of play. For each stage of such a game plan, a stage- specific evaluation function is induced, to be used by minimax search when playing the endgame. We aim at learning playing strategies that give good insight into the principles of playing specific endgames. Games played by these strategies should resemble human expert’s play in achieving goals and subgoals reliably, but not necessarily as quickly as possible. Keywords Machine learning . Computer chess . Long-term Strategy . Chess endgames . Chess databases 1. Introduction The standard approach used in chess playing programs relies on the minimax principle, efficiently implemented by alpha-beta algorithm, plus a heuristic evaluation function. This approach has proved to work particularly well in situations in which short-term tactics are prevalent, when evaluation function can easily recognise within the search horizon the con- sequences of good or bad moves. -
Draft – Not for Circulation
A Gross Miscarriage of Justice in Computer Chess by Dr. Søren Riis Introduction In June 2011 it was widely reported in the global media that the International Computer Games Association (ICGA) had found chess programmer International Master Vasik Rajlich in breach of the ICGA‟s annual World Computer Chess Championship (WCCC) tournament rule related to program originality. In the ICGA‟s accompanying report it was asserted that Rajlich‟s chess program Rybka contained “plagiarized” code from Fruit, a program authored by Fabien Letouzey of France. Some of the headlines reporting the charges and ruling in the media were “Computer Chess Champion Caught Injecting Performance-Enhancing Code”, “Computer Chess Reels from Biggest Sporting Scandal Since Ben Johnson” and “Czech Mate, Mr. Cheat”, accompanied by a photo of Rajlich and his wife at their wedding. In response, Rajlich claimed complete innocence and made it clear that he found the ICGA‟s investigatory process and conclusions to be biased and unprofessional, and the charges baseless and unworthy. He refused to be drawn into a protracted dispute with his accusers or mount a comprehensive defense. This article re-examines the case. With the support of an extensive technical report by Ed Schröder, author of chess program Rebel (World Computer Chess champion in 1991 and 1992) as well as support in the form of unpublished notes from chess programmer Sven Schüle, I argue that the ICGA‟s findings were misleading and its ruling lacked any sense of proportion. The purpose of this paper is to defend the reputation of Vasik Rajlich, whose innovative and influential program Rybka was in the vanguard of a mid-decade paradigm change within the computer chess community. -
Elo World, a Framework for Benchmarking Weak Chess Engines
Elo World, a framework for with a rating of 2000). If the true outcome (of e.g. a benchmarking weak chess tournament) doesn’t match the expected outcome, then both player’s scores are adjusted towards values engines that would have produced the expected result. Over time, scores thus become a more accurate reflection DR. TOM MURPHY VII PH.D. of players’ skill, while also allowing for players to change skill level. This system is carefully described CCS Concepts: • Evaluation methodologies → Tour- elsewhere, so we can just leave it at that. naments; • Chess → Being bad at it; The players need not be human, and in fact this can facilitate running many games and thereby getting Additional Key Words and Phrases: pawn, horse, bishop, arbitrarily accurate ratings. castle, queen, king The problem this paper addresses is that basically ACH Reference Format: all chess tournaments (whether with humans or com- Dr. Tom Murphy VII Ph.D.. 2019. Elo World, a framework puters or both) are between players who know how for benchmarking weak chess engines. 1, 1 (March 2019), to play chess, are interested in winning their games, 13 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn and have some reasonable level of skill. This makes 1 INTRODUCTION it hard to give a rating to weak players: They just lose every single game and so tend towards a rating Fiddly bits aside, it is a solved problem to maintain of −∞.1 Even if other comparatively weak players a numeric skill rating of players for some game (for existed to participate in the tournament and occasion- example chess, but also sports, e-sports, probably ally lose to the player under study, it may still be dif- also z-sports if that’s a thing). -
Super Human Chess Engine
SUPER HUMAN CHESS ENGINE FIDE Master / FIDE Trainer Charles Storey PGCE WORLD TOUR Young Masters Training Program SUPER HUMAN CHESS ENGINE Contents Contents .................................................................................................................................................. 1 INTRODUCTION ....................................................................................................................................... 2 Power Principles...................................................................................................................................... 4 Human Opening Book ............................................................................................................................. 5 ‘The Core’ Super Human Chess Engine 2020 ......................................................................................... 6 Acronym Algorthims that make The Storey Human Chess Engine ......................................................... 8 4Ps Prioritise Poorly Placed Pieces ................................................................................................... 10 CCTV Checks / Captures / Threats / Vulnerabilities ...................................................................... 11 CCTV 2.0 Checks / Checkmate Threats / Captures / Threats / Vulnerabilities ............................. 11 DAFiii Attack / Features / Initiative / I for tactics / Ideas (crazy) ................................................. 12 The Fruit Tree analysis process ............................................................................................................ -
Project: Chess Engine 1 Introduction 2 Peachess
P. Thiemann, A. Bieniusa, P. Heidegger Winter term 2010/11 Lecture: Concurrency Theory and Practise Project: Chess engine http://proglang.informatik.uni-freiburg.de/teaching/concurrency/2010ws/ Project: Chess engine 1 Introduction From the Wikipedia article on Chess: Chess is a two-player board game played on a chessboard, a square-checkered board with 64 squares arranged in an eight-by-eight grid. Each player begins the game with sixteen pieces: one king, one queen, two rooks, two knights, two bishops, and eight pawns. The object of the game is to checkmate the opponent’s king, whereby the king is under immediate attack (in “check”) and there is no way to remove or defend it from attack on the next move. The game’s present form emerged in Europe during the second half of the 15th century, an evolution of an older Indian game, Shatranj. Theoreticians have developed extensive chess strategies and tactics since the game’s inception. Computers have been used for many years to create chess-playing programs, and their abilities and insights have contributed significantly to modern chess theory. One, Deep Blue, was the first machine to beat a reigning World Chess Champion when it defeated Garry Kasparov in 1997. Do not worry if you are not familiar with the rules of chess! You are not asked to change the methods which calculate the moves, nor the internal representation of boards or moves, nor the scoring of boards. 2 Peachess Peachess is a chess engine written in Java. The implementation consists of several components. 2.1 The chess board The chess board representation stores the actual state of the game. -
Kasparov's Nightmare!! Bobby Fischer Challenges IBM to Simul IBM
California Chess Journal Volume 20, Number 2 April 1st 2004 $4.50 Kasparov’s Nightmare!! Bobby Fischer Challenges IBM to Simul IBM Scrambles to Build 25 Deep Blues! Past Vs Future Special Issue • Young Fischer Fires Up S.F. • Fischer commentates 4 Boyscouts • Building your “Super Computer” • Building Fischer’s Dream House • Raise $500 playing chess! • Fischer Articles Galore! California Chess Journal Table of Con tents 2004 Cal Chess Scholastic Championships The annual scholastic tourney finishes in Santa Clara.......................................................3 FISCHER AND THE DEEP BLUE Editor: Eric Hicks Contributors: Daren Dillinger A miracle has happened in the Phillipines!......................................................................4 FM Eric Schiller IM John Donaldson Why Every Chess Player Needs a Computer Photographers: Richard Shorman Some titles speak for themselves......................................................................................5 Historical Consul: Kerry Lawless Founding Editor: Hans Poschmann Building Your Chess Dream Machine Some helpful hints when shopping for a silicon chess opponent........................................6 CalChess Board Young Fischer in San Francisco 1957 A complet accounting of an untold story that happened here in the bay area...................12 President: Elizabeth Shaughnessy Vice-President: Josh Bowman 1957 Fischer Game Spotlight Treasurer: Richard Peterson One game from the tournament commentated move by move.........................................16 Members at -
March 2020 Uschess.Org the United States’ Largest Chess Specialty Retailer
March 2020 USChess.org The United States’ Largest Chess Specialty Retailer 888.51.CHESS (512.4377) www.USCFSales.com ƩĂĐŬŝŶŐǁŝƚŚŐϮͲŐϰ The 100 Endgames You Must Know Workbook The Modern Way to Get the Upper Hand in Chess WƌĂĐƟĐĂůŶĚŐĂŵĞƐdžĞƌĐŝƐĞƐĨŽƌǀĞƌLJŚĞƐƐWůĂLJĞƌ Dmitry Kryavkin 288 pages - $24.95 Jesus de la Villa 288 pages - $24.95 dŚĞƉĂǁŶƚŚƌƵƐƚŐϮͲŐϰŝƐĂƉĞƌĨĞĐƚǁĂLJƚŽĐŽŶĨƵƐĞLJŽƵƌ ͞/ůŽǀĞƚŚŝƐŬ͊/ŶŽƌĚĞƌƚŽŵĂƐƚĞƌĞŶĚŐĂŵĞƉƌŝŶĐŝƉůĞƐLJŽƵ ŽƉƉŽŶĞŶƚƐĂŶĚĚŝƐƌƵƉƚƚŚĞŝƌƉŽƐŝƟŽŶ͘tŝƚŚůŽƚƐŽĨŝŶƐƚƌƵĐƟǀĞ ǁŝůůŶĞĞĚƚŽƉƌĂĐƟĐĞƚŚĞŵ͘͟ʹNM Han Schut, Chess.com ĞdžĂŵƉůĞƐ'D<ƌLJĂŬǀŝŶƐŚŽǁƐŚŽǁŝƚĐĂŶďĞƵƐĞĚƚŽĚĞĨĞĂƚ ͞dŚĞƉĞƌĨĞĐƚƐƵƉƉůĞŵĞŶƚƚŽĞůĂsŝůůĂ͛ƐŵĂŶƵĂů͘dŽŐĂŝŶ ůĂĐŬŝŶƚŚĞƵƚĐŚ͕ƚŚĞYƵĞĞŶ͛Ɛ'Ăŵďŝƚ͕ƚŚĞEŝŵnjŽͲ/ŶĚŝĂŶ͕ ƐƵĸĐŝĞŶƚŬŶŽǁůĞĚŐĞŽĨƚŚĞŽƌĞƟĐĂůĞŶĚŐĂŵĞƐLJŽƵƌĞĂůůLJŽŶůLJ ƚŚĞ<ŝŶŐ͛Ɛ/ŶĚŝĂŶ͕ƚŚĞ^ůĂǀĂŶĚƚŚĞŶŐůŝƐŚKƉĞŶŝŶŐ͘>ĞĂƌŶ ŶĞĞĚƚǁŽďŽŽŬƐ͘͟ʹIM Herman Grooten, Schaaksite NEW! ƚŚĞƚLJƉŝĐĂůǁĂLJƐƚŽŐĂŝŶƚĞŵƉŝ͕ŬĞĞƉƚŚĞŵŽŵĞŶƚƵŵĂŶĚ ŵĂdžŝŵŝnjĞLJŽƵƌŽƉƉŽŶĞŶƚ͛ƐƉƌŽďůĞŵƐ͘ Strategic Chess Exercises Keep It Simple 1.d4 Find the Right Way to Outplay Your Opponent ^ŽůŝĚĂŶĚ^ƚƌĂŝŐŚƞŽƌǁĂƌĚŚĞƐƐKƉĞŶŝŶŐZĞƉĞƌƚŽŝƌĞĨŽƌtŚŝƚĞ Emmanuel Bricard 224 pages - $24.95 Christof Sielecki 432 pages - $29.95 &ŝŶĂůůLJĂŶĞdžĞƌĐŝƐĞƐŬƚŚĂƚŝƐŶŽƚĂďŽƵƚƚĂĐƟĐƐ͊ ^ŝĞůĞĐŬŝ͛ƐƌĞƉĞƌƚŽŝƌĞǁŝƚŚϭ͘ĚϰŵĂLJďĞĞǀĞŶĞĂƐŝĞƌƚŽ ͞ƌŝĐĂƌĚŝƐĐůĞĂƌůLJĂǀĞƌLJŐŝŌĞĚƚƌĂŝŶĞƌ͘,ĞƐĞůĞĐƚĞĚĂƐƵƉĞƌď ŵĂƐƚĞƌƚŚĂŶŚŝƐϭ͘ĞϰƌĞĐŽŵŵĞŶĚĂƟŽŶƐ͕ďĞĐĂƵƐĞŝƚŝƐƐƵĐŚĂ ƌĂŶŐĞŽĨƉŽƐŝƟŽŶƐĂŶĚĞdžƉůĂŝŶƐƚŚĞƐŽůƵƟŽŶƐĞdžƚƌĞŵĞůLJ ĐŽŚĞƌĞŶƚƐLJƐƚĞŵ͗ƚŚĞŵĂŝŶĐŽŶĐĞƉƚŝƐĨŽƌtŚŝƚĞƚŽƉůĂLJϭ͘Ěϰ͕ ǁĞůů͘͟ʹGM Daniel King Ϯ͘EĨϯ͕ϯ͘Őϯ͕ϰ͘ŐϮ͕ϱ͘ϬͲϬĂŶĚŝŶŵŽƐƚĐĂƐĞƐϲ͘Đϰ͘ ͞&ŽƌĐŚĞƐƐĐŽĂĐŚĞƐƚŚŝƐŬŝƐŶŽƚŚŝŶŐƐŚŽƌƚŽĨƉŚĞŶŽŵĞŶĂů͘͟ ͞Ɛ/ƚŚŝŶŬƚŚĂƚ/ƐŚŽƵůĚŬĞĞƉŵLJĂĚǀŝĐĞ͚ƐŝŵƉůĞ͕͛/ǁŽƵůĚƐĂLJ -
The TCEC Cup 2 Report
TCEC Cup 2 Article Accepted Version The TCEC Cup 2 report Haworth, G. and Hernandez, N. (2019) TCEC Cup 2. ICGA Journal, 41 (2). pp. 100-107. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-190104 Available at http://centaur.reading.ac.uk/81390/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . Published version at: https://content.iospress.com/articles/icga-journal/icg190104 To link to this article DOI: http://dx.doi.org/10.3233/ICG-190104 Publisher: The International Computer Games Association All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online TCEC Cup 2 Guy Haworth and Nelson Hernandez1 Reading, UK and Maryland, USA The knockout format of TCEC Cup 1 (Haworth and Hernandez, 2019a/b) was well received by its audience and was adopted as a regular interlude between the TCEC Seasons’ Division P and Superfinal. TCEC Cup 2 was nested within TCEC14 (Haworth and Hernandez, 2019c/d) and began on January 17th 2019 with 32 chess engines and only a few minor changes from the inaugural Cup event. The ‘standard pairing’ was again used, with seed s playing seed 26-r-s+1 in round r if the wins all go to the higher seed. -
Distributional Differences Between Human and Computer Play at Chess
Multidisciplinary Workshop on Advances in Preference Handling: Papers from the AAAI-14 Workshop Human and Computer Preferences at Chess Kenneth W. Regan Tamal Biswas Jason Zhou Department of CSE Department of CSE The Nichols School University at Buffalo University at Buffalo Buffalo, NY 14216 USA Amherst, NY 14260 USA Amherst, NY 14260 USA [email protected] [email protected] Abstract In our case the third parties are computer chess programs Distributional analysis of large data-sets of chess games analyzing the position and the played move, and the error played by humans and those played by computers shows the is the difference in analyzed value from its preferred move following differences in preferences and performance: when the two differ. We have run the computer analysis (1) The average error per move scales uniformly higher the to sufficient depth estimated to have strength at least equal more advantage is enjoyed by either side, with the effect to the top human players in our samples, depth significantly much sharper for humans than computers; greater than used in previous studies. We have replicated our (2) For almost any degree of advantage or disadvantage, a main human data set of 726,120 positions from tournaments human player has a significant 2–3% lower scoring expecta- played in 2010–2012 on each of four different programs: tion if it is his/her turn to move, than when the opponent is to Komodo 6, Stockfish DD (or 5), Houdini 4, and Rybka 3. move; the effect is nearly absent for computers. The first three finished 1-2-3 in the most recent Thoresen (3) Humans prefer to drive games into positions with fewer Chess Engine Competition, while Rybka 3 (to version 4.1) reasonable options and earlier resolutions, even when playing was the top program from 2008 to 2011.