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Deep Fritz 11 Rating
Deep fritz 11 rating CCRL 40/4 main list: Fritz 11 has no rank with rating of Elo points (+9 -9), Deep Shredder 12 bit (+=18) + 0 = = 0 = 1 0 1 = = = 0 0 = 0 = = 1 = = 1 = = 0 = = 0 1 0 = 0 1 = = 1 1 = – Deep Sjeng WC bit 4CPU, , +12 −13, (−65), 24 − 13 (+13−2=22). %. This is one of the 15 Fritz versions we tested: Compare them! . − – Deep Sjeng bit, , +22 −22, (−15), 19 − 15 (+13−9=12). % / Complete rating list. CCRL 40/40 Rating List — All engines (Quote). Ponder off Deep Fritz 11 4CPU, , +29, −29, %, −, %, %. Second on the list is Deep Fritz 11 on the same hardware, points below The rating list is compiled by a Swedish group on the basis of. Fritz is a German chess program developed by Vasik Rajlich and published by ChessBase. Deep Fritz 11 is eighth on the same list, with a rating of Overall the answer for what to get is depending on the rating Since I don't have Rybka and I do have Deep Fritz 11, I'll vote for game vs Fritz using Opening or a position? - Chess. Find helpful customer reviews and review ratings for Fritz 11 Chess Playing Software for PC at There are some algorithms, from Deep fritz I have a small Engine Match database and use the Fritz 11 Rating Feature. Could somebody please explain the logic behind the algorithm? Chess games of Deep Fritz (Computer), career statistics, famous victories, opening repertoire, PGN download, Feb Version, Cores, Rating, Rank. DEEP Rybka playing against Fritz 11 SE using Chessbase We expect, that not only the rating figures, but also the number of games and the 27, Deep Fritz 11 2GB Q 2,4 GHz, , 18, , , 62%, Deep Fritz. -
2009 U.S. Tournament.Our.Beginnings
Chess Club and Scholastic Center of Saint Louis Presents the 2009 U.S. Championship Saint Louis, Missouri May 7-17, 2009 History of U.S. Championship “pride and soul of chess,” Paul It has also been a truly national Morphy, was only the fourth true championship. For many years No series of tournaments or chess tournament ever held in the the title tournament was identi- matches enjoys the same rich, world. fied with New York. But it has turbulent history as that of the also been held in towns as small United States Chess Championship. In its first century and a half plus, as South Fallsburg, New York, It is in many ways unique – and, up the United States Championship Mentor, Ohio, and Greenville, to recently, unappreciated. has provided all kinds of entertain- Pennsylvania. ment. It has introduced new In Europe and elsewhere, the idea heroes exactly one hundred years Fans have witnessed of choosing a national champion apart in Paul Morphy (1857) and championship play in Boston, and came slowly. The first Russian Bobby Fischer (1957) and honored Las Vegas, Baltimore and Los championship tournament, for remarkable veterans such as Angeles, Lexington, Kentucky, example, was held in 1889. The Sammy Reshevsky in his late 60s. and El Paso, Texas. The title has Germans did not get around to There have been stunning upsets been decided in sites as varied naming a champion until 1879. (Arnold Denker in 1944 and John as the Sazerac Coffee House in The first official Hungarian champi- Grefe in 1973) and marvelous 1845 to the Cincinnati Literary onship occurred in 1906, and the achievements (Fischer’s winning Club, the Automobile Club of first Dutch, three years later. -
Chess2vec: Learning Vector Representations for Chess
Chess2vec: Learning Vector Representations for Chess Berk Kapicioglu Ramiz Iqbal∗ Tarik Koc OccamzRazor MD Anderson Cancer Center OccamzRazor [email protected] [email protected] [email protected] Louis Nicolas Andre Katharina Sophia Volz OccamzRazor OccamzRazor [email protected] [email protected] Abstract We conduct the first study of its kind to generate and evaluate vector representations for chess pieces. In particular, we uncover the latent structure of chess pieces and moves, as well as predict chess moves from chess positions. We share preliminary results which anticipate our ongoing work on a neural network architecture that learns these embeddings directly from supervised feedback. 1 Introduction In the last couple of years, advances in machine learning have yielded dramatic improvements in tasks as diverse as visual object classification [9], automatic speech recognition [7], machine translation [18], and natural language processing (NLP) [12]. A common thread among all these tasks is, as diverse as they may seem, they all involve processing input data, such as image, audio, and text, that have not traditionally been amenable to feature engineering. Modern machine learning methods that enabled these breakthroughs did so partly because they shifted the burden away from feature engineering, which is difficult for humans and requires domain expertise, towards designing models that automatically infer feature representations that are relevant for downstream tasks [1]. In this paper, we are interested in learning and studying feature representations of chess positions and pieces. Our work is inspired by how learning vector representation of words [12, 13], also known as word embeddings, yielded improvements in tasks such as syntactic parsing [16] and sentiment analysis [17]. -
Issue 16, June 2019 -...CHESSPROBLEMS.CA
...CHESSPROBLEMS.CA Contents 1 Originals 746 . ISSUE 16 (JUNE 2019) 2019 Informal Tourney....... 746 Hors Concours............ 753 2 Articles 755 Andreas Thoma: Five Pendulum Retros with Proca Anticirce.. 755 Jeff Coakley & Andrey Frolkin: Multicoded Rebuses...... 757 Arno T¨ungler:Record Breakers VIII 766 Arno T¨ungler:Pin As Pin Can... 768 Arno T¨ungler: Circe Series Tasks & ChessProblems.ca TT9 ... 770 3 ChessProblems.ca TT10 785 4 Recently Honoured Canadian Compositions 786 5 My Favourite Series-Mover 800 6 Blast from the Past III: Checkmate 1902 805 7 Last Page 808 More Chess in the Sky....... 808 Editor: Cornel Pacurar Collaborators: Elke Rehder, . Adrian Storisteanu, Arno T¨ungler Originals: [email protected] Articles: [email protected] Chess drawing by Elke Rehder, 2017 Correspondence: [email protected] [ c Elke Rehder, http://www.elke-rehder.de. Reproduced with permission.] ISSN 2292-8324 ..... ChessProblems.ca Bulletin IIssue 16I ORIGINALS 2019 Informal Tourney T418 T421 Branko Koludrovi´c T419 T420 Udo Degener ChessProblems.ca's annual Informal Tourney Arno T¨ungler Paul R˘aican Paul R˘aican Mirko Degenkolbe is open for series-movers of any type and with ¥ any fairy conditions and pieces. Hors concours compositions (any genre) are also welcome! ! Send to: [email protected]. " # # ¡ 2019 Judge: Dinu Ioan Nicula (ROU) ¥ # 2019 Tourney Participants: ¥!¢¡¥£ 1. Alberto Armeni (ITA) 2. Rom´eoBedoni (FRA) C+ (2+2)ser-s%36 C+ (2+11)ser-!F97 C+ (8+2)ser-hsF73 C+ (12+8)ser-h#47 3. Udo Degener (DEU) Circe Circe Circe 4. Mirko Degenkolbe (DEU) White Minimummer Leffie 5. Chris J. Feather (GBR) 6. -
Life Cycle Patterns of Cognitive Performance Over the Long
Life cycle patterns of cognitive performance over the long run Anthony Strittmattera,1 , Uwe Sundeb,1,2, and Dainis Zegnersc,1 aCenter for Research in Economics and Statistics (CREST)/Ecole´ nationale de la statistique et de l’administration economique´ Paris (ENSAE), Institut Polytechnique Paris, 91764 Palaiseau Cedex, France; bEconomics Department, Ludwig-Maximilians-Universitat¨ Munchen,¨ 80539 Munchen,¨ Germany; and cRotterdam School of Management, Erasmus University, 3062 PA Rotterdam, The Netherlands Edited by Robert Moffit, John Hopkins University, Baltimore, MD, and accepted by Editorial Board Member Jose A. Scheinkman September 21, 2020 (received for review April 8, 2020) Little is known about how the age pattern in individual perfor- demanding tasks, however, and are limited in terms of compara- mance in cognitively demanding tasks changed over the past bility, technological work environment, labor market institutions, century. The main difficulty for measuring such life cycle per- and demand factors, which all exhibit variation over time and formance patterns and their dynamics over time is related to across skill groups (1, 19). Investigations that account for changes the construction of a reliable measure that is comparable across in skill demand have found evidence for a peak in performance individuals and over time and not affected by changes in technol- potential around ages of 35 to 44 y (20) but are limited to short ogy or other environmental factors. This study presents evidence observation periods that prevent an analysis of the dynamics for the dynamics of life cycle patterns of cognitive performance of the age–performance profile over time and across cohorts. over the past 125 y based on an analysis of data from profes- An additional problem is related to measuring productivity or sional chess tournaments. -
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. -
Yale Yechiel N. Robinson, Patent Attorney Email: [email protected]
December 16, 2019 From: Yale Yechiel N. Robinson, Patent Attorney Email: [email protected] To: United States Patent and Trademark Office (USPTO) Email: [email protected] Re: Request for Comments on Intellectual Property Protection for Artificial Intelligence Innovation, Docket number: PTO-C-2019-0038 To the USPTO and Members of the Public: My name is Yale Robinson. In addition to my professional work as a patent attorney and in other legal matters, I have for years enjoyed the intergenerational conversation within the game of chess, which originated many centuries ago and spread across Europe and North America around the middle of the 19th century, and has since reached every major country in the world. I will direct my comments regarding issues of intellectual property for artificial intelligence innovations based on the history of traditional western chess. Early innovators in computing, including Alan Turing and Claude Shannon, tried to calculate the value of certain chess positions using the 1950-era equivalent of a present-day handheld calculator. Their work launched the age of computer chess, where the strongest present-day computers can consistently (but not always) defeat or draw elite human grandmasters. The quality of human-versus-human play has increased in part because of computer analysis of opening and middle-game positions that strong players can analyze and practice playing on their home computer device before trying the idea against a human player in competition. In addition to the development of chess theory for the benefit of chess players (human and electronic alike), the impact of chess computing has arguably extended beyond the limits of the game itself. -
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 ............................................................................................................ -
Table of Contents 129
Table of Contents 129 TABLE OF CONTENTS Table of Contents ......................................................................................................................................................129 Science and Checkers (H.J. van den Herik) .............................................................................................................129 Searching Solitaire in Real Time (R. Bjarnason, P. Tadepalli, and A. Fern)........................................................ 131 An Efficient Approach to Solve Mastermind Optimally (L-T. Huang, S-T. Chen, S-Ch. Huang, and S.-S. Lin) ...................................................................................................................................... 143 Note: ................................................................................................................................................................. 150 Gentlemen, Stop your Engines! (G. McC. Haworth).......................................................................... 150 Information for Contributors............................................................................................................................. 157 News, Information, Tournaments, and Reports: ......................................................................................................158 The 12th Computer Olympiad (Continued) (H.J. van den Herik, M.H.M. Winands, and J. Hellemons).158 DAM 2.2 Wins Draughts Tournament (T. Tillemans) ........................................................................158 -
Oral History of Hans Berliner
Oral History of Hans Berliner Interviewed by: Gardner Hendrie Recorded: March 7, 2005 Riviera Beach, Florida Total Running Time: 2:33:00 CHM Reference number: X3131.2005 © 2005 Computer History Museum CHM Ref: X3131.2005 © 2005 Computer History Museum Page 1 of 65 Q: Who has graciously agreed to do an oral history for the Computer History Museum. Thank you very much, Hans. Hans Berliner: Oh, you’re most welcome. Q: O.k. I think where I’d like to start is maybe a little further back than you might expect. I’d like to know if you could share with us a little bit about your family background. The environment that you grew up in. Your mother and father, what they did. Your brothers and sisters. Hans Berliner: O.k. Q: Where you were born. That sort of thing. Hans Berliner: O.k. I was born in Berlin in 1929, and we immigrated to the United States, very fortunately, in 1937, to Washington, D.C. As far as the family goes, my great uncle, who was my grandfather’s brother, was involved in telephone work at the turn of the previous century. And he actually owned the patent on the carbon receiver for the telephone. And they started a telephone company in Hanover, Germany, based upon his telephone experience. And he, later on, when Edison had patented the cylinder for recording, he’d had enough experience with sound recording that he said, “that’s pretty stupid”. And he decided to do the recording on a disc, and he successfully defended his patent in the Supreme Court, and so the patent on the phono disc belongs to Emile Berliner, who was my grand uncle. -
AI from a Game Player Perspective Eleonora Giunchiglia Why?
AI from a game player perspective Eleonora Giunchiglia Why? David Churchill, professor at Memorial University of Newfoundland: ``From a scientific point of view, the properties of StarCraft are very much like the properties of real life. [. .] We’re making a test bed for technologies we can use in the real world.’’ This concept can be extended to every game and justifies the research of AI in games. Connections with MAS A multiagent system is one composed of multiple interacting software components known as agents, which are typically capable of cooperating to solve problems that are beyond the abilities of any individual member. This represents by far a more complex setting than the traditional 1vs1 games à only in recent years they were able to study games with multiple agents. In this project we traced the path that led from AI applied to 1vs1 games to many vs. many games. The very first attempts The very first attempts were done even before the concept of Artificial intelligence was born: 1890: Leonardo Torres y Quevedo developed an electro-mechanical device, El Ajedrecista, to checkmate a human opponent’s king using only its own king and rook. 1948: Alan Turing wrote the algorithm TurboChamp. He never managed to run it on a real computer. The very first attempts 1950: Claude Shannon proposes the Minimax algorithm. Shannon proposed two different ways of deciding the next move: 1. doing brute-force tree search on the complete tree, and take the optimal move, or 2. looking at a small subset of next moves at each layer during tree search, and take the “likely optimal” move. -
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