Chess software and its impact on chess players

Computer-aided chess is an important teaching method, as it allows a student to play under every condition possible, and regulates the speed of his/her development at an incremental pace, measured against actual players in the rated chess community. It is also relatively inexpensive, and pervasive, and allows players to match themselves against competitors from across the world. The learning process extends beyond games, as interactive software has shown it teaches several skills, such as opening, strategy, tactics, and chess-problem solving. Furthermore, current applications allow chess players to establish rankings via online chess tournaments, meet international grandmasters, and have access to training tools based on strategies from chess masters. Using 250 chess software packages, this research classifies them into distinct categories based mainly on the Gobet and Jansen's organization of the chess knowledge. This is followed by extensive discussion that analyzes these training tools, in order to identify the best training techniques available building on a research on human computer interaction, cognitive psychology, and chess theory. --P.ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b151379

Investigating evolutionary checkers by incorporating individual and social learning, N-tuple systems and a round robin tournament

In recent years, much research attention has been paid to evolving self-learning game players. Fogel's Blondie24 is just one demonstration of a real success in this field and it has inspired many other scientists. In this thesis, artificial neural networks are employed to evolve game playing strategies for the game of checkers by introducing a league structure into the learning phase of a system based on Blondie24. We believe that this helps eliminate some of the randomness in the evolution. The best player obtained is tested against an evolutionary checkers program based on Blondie24. The results obtained are promising. In addition, we introduce an individual and social learning mechanism into the learning phase of the evolutionary checkers system. The best player obtained is tested against an implementation of an evolutionary checkers program, and also against a player, which utilises a round robin tournament. The results are promising. N-tuple systems are also investigated and are used as position value functions for the game of checkers. The architecture of the n-tuple is utilises temporal difference learning. The best player obtained is compared with an implementation of evolutionary checkers program based on Blondie24, and also against a Blondie24 inspired player, which utilises a round robin tournament. The results are promising. We also address the question of whether piece difference and the look-ahead depth are important factors in the Blondie24 architecture. Our experiments show that piece difference and the look-ahead depth have a significant effect on learning abilities

Investigating evolutionary checkers by incorporating individual and social learning, N-tuple systems and a round robin tournament

In recent years, much research attention has been paid to evolving self-learning game players. Fogel's Blondie24 is just one demonstration of a real success in this field and it has inspired many other scientists. In this thesis, artificial neural networks are employed to evolve game playing strategies for the game of checkers by introducing a league structure into the learning phase of a system based on Blondie24. We believe that this helps eliminate some of the randomness in the evolution. The best player obtained is tested against an evolutionary checkers program based on Blondie24. The results obtained are promising. In addition, we introduce an individual and social learning mechanism into the learning phase of the evolutionary checkers system. The best player obtained is tested against an implementation of an evolutionary checkers program, and also against a player, which utilises a round robin tournament. The results are promising. N-tuple systems are also investigated and are used as position value functions for the game of checkers. The architecture of the n-tuple is utilises temporal difference learning. The best player obtained is compared with an implementation of evolutionary checkers program based on Blondie24, and also against a Blondie24 inspired player, which utilises a round robin tournament. The results are promising. We also address the question of whether piece difference and the look-ahead depth are important factors in the Blondie24 architecture. Our experiments show that piece difference and the look-ahead depth have a significant effect on learning abilities

Recent Advances in General Game Playing

The goal of General Game Playing (GGP) has been to develop computer programs that can perform well across various game types. It is natural for human game players to transfer knowledge from games they already know how to play to other similar games. GGP research attempts to design systems that work well across different game types, including unknown new games. In this review, we present a survey of recent advances (2011 to 2014) in GGP for both traditional games and video games. It is notable that research on GGP has been expanding into modern video games. Monte-Carlo Tree Search and its enhancements have been the most influential techniques in GGP for both research domains. Additionally, international competitions have become important events that promote and increase GGP research. Recently, a video GGP competition was launched. In this survey, we review recent progress in the most challenging research areas of Artificial Intelligence (AI) related to universal game playing

The Blondie25 Chess Program Competes Against Fritz 8.0 and a Human Chess Master

Abstract- Previous research on the use of coevolution to improve a baseline chess program demonstrated a performance rating of 2650 against Pocket Fritz 2.0 based on 16 games played (13 wins, 0 losses, 3 draws). The resultant program, named Blondie25, did not use any rules for managing the time allocated per move; it simply used three minutes on each move. Heuristics to more effectively manage time were developed by trial and error, play testing against Fritz 8.0. The best heuristics discovered were different for black and white. The results of 12 games played on each side were 1 win, 4 losses, and 7 draws for black, and 2 wins, 6 losses, and 4 draws for white. Fritz 8.0 is rated currently at 2752 (±20) on SSDF (the acronym for the Swedish Chess Computer Association), placing it as the 12 th strongest program in the world. At the time of the contest between Blondie25 and Fritz 8.0, Fritz 8.0 was rated #5 in the world. The results are the first case of an evolved chess program defeating a world-class chess program (three times). The performance rating for Blondie25 against Fritz 8.0 was 2635.33, which compares well with the previous performance rating of 2650 against Pocket Fritz 2.0. Blondie25 was then tested against a nationally ranked human chess master, rated 2301. In four games, Blondie25 won three and lost one.