WCCC 2016: the 22nd World Computer Chess Championship

In the ICGA's 22nd World Computer Chess Championship in Leiden, six contestants together gave the latest demonstration of the state of the art in chess-playing programs. This article highlights the advances and subtleties exemplified by their play. The new World Champion was KOMODO with JONNY, edged out in the play-off, second and SHREDDER third. GRIDGINKGO, HIARCS and RAPTOR took the remaining places

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

Search versus Knowledge: An Empirical Study of Minimax on KRK

This article presents the results of an empirical experiment designed to gain insight into what is the effect of the minimax algorithm on the evaluation function. The experiment’s simulations were performed upon the KRK chess endgame. Our results show that dependencies between evaluations of sibling nodes in a game tree and an abundance of possibilities to commit blunders present in the KRK endgame are not sufficient to explain the success of the minimax principle in practical game-playing as was previously believed. The article shows that minimax in combination with a noisy evaluation function introduces a bias into the backed-up evaluations and argues that this bias is what masked the effectiveness of the minimax in previous studies

Chess Endgames and Neural Networks

The existence of endgame databases challenges us to extract higher-grade information and knowledge from their basic data content. Chess players, for example, would like simple and usable endgame theories if such holy grail exists: endgame experts would like to provide such insights and be inspired by computers to do so. Here, we investigate the use of artificial neural networks (NNs) to mine these databases and we report on a first use of NNs on KPK. The results encourage us to suggest further work on chess applications of neural networks and other data-mining techniques