Kolloquium Donnerstag, 27.6. 14:15 Uhr, WE5/05.013 

Christian Massny (MA WI): Training by Error - Chess Training by Individual Error Classification   

Chess Engines have long surpassed all but the most potent of human chess players. The vast majority of chess players cannot even come close to beating any given engine on a personal computer with mainstream hardware.Hence chess programs are mostly used for two purposes: analyzing past games and getting used to playing new openings. The analysis is mostly done by hand, importing any past game and the replaying it with the calculations of the engine showing when mistakes were made in the game.The goal of this thesis now is to analyze past games on a larger scale using the complete game database of a particular player. The software to be programmed will import any number of games and analyze them. The analysis will both find mistakes made by the player using an open source chess engine and then classify the mistakes the player made so that statistical information regarding the type and magnitude of mistakes the player makes will be available for further training.Each mistake can be singled out and analyzed individually. At the end of this thesis the author will propose a way of using the error database to teach the player to get rid of his mistakes even better.