This contribution reports about an almost forgotten attempt to find themes in chess composition automatically by machine. Based on investigations of the late International Master of Chess Composition Dieter Müller in the 1970s, the writers describe some of his results. They hope to fill a gap in the great history of chess programming.

Since DeepMind’s AlphaZero, Zero learning quickly became the state-of-the-art method for many board games. It can be improved using a fully convolutional structure (no fully connected layer). Using such an architecture plus global pooling, we can create bots independent of the board size. The training can be made more robust by keeping track of the best checkpoints during the training and by training

This article is based on my personal reminiscences about the early days of computer chess tournaments, describing not only how different the technology was, but also that progress was steady and continues today in the broader field of Artificial Intelligence. The author was a participant in the 1stACM computer chess championship (1970) and continued to compete well into the 1980s. Speaking for myself

It is been 46 years since the first computer chess world championship was held in Stockholm in 1974, and there have now been 24 more with over 700 games played. This paper investigates a number of questions that a curious soul might ask regarding the results of these games, especially how they ended. The data provided herein attempts to answer several related questions. In addition, it presents data

BeBe was one of the highest-performing chess programs of the 1980s. This article relates some of the fond computer chess memories from Linda Scherzer, co-author of BeBe.

On August 31, 1970, an experiment began that continues to this day. The first chess tournament for computers was held as part of the Association for Computing Machinery’s (ACM’s) National Conference. The interest generated was tremendous, leading to ACM sponsoring an annual event until 1994. Chesscompetitions continue to this day, allowing for 50 years of data on the growth of artificial intelligence

On July 24–25, 1976, the computer program Chess 4.5 participated in the Paul Masson Chess Classic Tournament. The program achieved an impressive result. This article describes my recollection of this event and its aftermath.

The game of chess involves patterns. In this article we develop a simple description language for chess positions as patterns. It seems useful to catalogue similar positions in a generic form, based on common elements, in order to simplify the search for chess positions. A simple chess pattern language is used here to develop a computer program to find similar chess configurations. The aim is to avoid

2048-like games are a family of single-player stochastic puzzle games, which consist of sliding numbered-tiles that combine to form tiles with larger numbers. Notable examples of games in this family include Threes!, 2048, and 2584. 2048-like games are highly suitable for educational purposes due to their simplicity and popularity. Numerous machine learning methods have been proposed to play 2048-like

A strategy to find fair time controls for Armageddon chess games is presented and exemplified with 2750+ ELO players. The research was divided in two stages. The first one consisted in finding a time control for Stockfish 9 so that it played with an effective strength equal to the average 2750+ ELOplayers of the 2017 World Blitz Chess Championship. Analysis of this stage showed that a fair time control

Stalemating the opponent in chess has given rise to various opinions as to the nature of that result and the reward it should properly receive. Here, following Lasker and Reti, we propose that ‘stalemate’ is a secondary goal, superior to a draw by agreement or rule – but inferior to mate. We reportthe work of ‘Aloril’ who has created endgame tables holding both ‘DTM’ depth to mate and ‘DTS’ depth to