WSCC 2017: the World Speed Computer Chess Championship

WSCC is the ICGA's World Speed Computer Chess Championship, held at 'blitz' 5'+5" tempo in parallel with the similar WCCC (Computer Chess) and common-platform WCSC (Chess Software) competitive computational experiments. Even at this tempo, the games are super-GM quality. The title was taken this time by Komodo with Jonny and Shredder also on the podium

WCCC 2017: the 23rd world computer chess championship

The ICGA's 23rd World Computer Chess Championship started on July 3rd. 2017. The competitors in this select field were CHIRON, JONNY, KOMODO and SHREDDER. The contest was close and set new standards for the event: all podium places required play-offs. Ultimately, KOMODO retained its title, beating JONNY and SHREDDER. The analysis of the games and the pgn file of games are provided here

Turing, Kasparov and the future

The 'Turing 100' Conference in Manchester was the main event of the Turing Centenary Year in 2012. This is a report and reflection on Kasparov's popular talk. Within it, he explained how Turing and influenced computer chess, his career and the chess community. Kasparov also played Chessbase's 'TURING' emulation of Turing's second paper chess engine, here labelled 'AT2'. Quasi Turing-tests, computer contributions to world championship chess, and suspected cheating in chess are also mentioned

The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments

International audienceTHE AUTHORS ARE EXTREMELY GRATEFUL TO GRID5000 for helping in designing and experimenting around Monte-Carlo Tree Search. In order to promote computer Go and stimulate further development and research in the field, the event activities, "Computational Intelligence Forum" and "World 99 Computer Go Championship," were held in Taiwan. This study focuses on the invited games played in the tournament, "Taiwanese Go players versus the computer program MoGo," held at National University of Tainan (NUTN). Several Taiwanese Go players, including one 9-Dan professional Go player and eight amateur Go players, were invited by NUTN to play against MoGo from August 26 to October 4, 2008. The MoGo program combines All Moves As First (AMAF)/Rapid Action Value Estimation (RAVE) values, online "UCT-like" values, offline values extracted from databases, and expert rules. Additionally, four properties of MoGo are analyzed including: (1) the weakness in corners, (2) the scaling over time, (3) the behavior in handicap games, and (4) the main strength of MoGo in contact fights. The results reveal that MoGo can reach the level of 3 Dan with, (1) good skills for fights, (2) weaknesses in corners, in particular for "semeai" situations, and (3) weaknesses in favorable situations such as handicap games. It is hoped that the advances in artificial intelligence and computational power will enable considerable progress in the field of computer Go, with the aim of achieving the same levels as computer chess or Chinese chess in the future

Playing Cassino with Reinforcement Learning

Reinforcement learning algorithms have been used to create game-playing agents for various games—mostly, deterministic games such as chess, shogi, and Go. This study used Deep-Q reinforcement learning to create an agent that plays a non-deterministic card game, Cassino. This agent’s performance was compared against the performance of a Cassino mobile app. Results showed that the trained models did not perform well and had trouble training around build actions which are important in Cassino. Future research could experiment with other reinforcement learning algorithms to see if they are better at training around build actions

Spartan Daily, December 5, 1980

Volume 75, Issue 66https://scholarworks.sjsu.edu/spartandaily/6701/thumbnail.jp

Spartan Daily, November 6, 1992

Volume 99, Issue 50https://scholarworks.sjsu.edu/spartandaily/8334/thumbnail.jp