Researchers from CREF and the Historical Archive of the Pontifical Gregorian University used a novel approach that combines machine learning and spectroscopic techniques to understand the genesis of an ancient book via handwriting attribution.
Quantifying the complexity and similarity of chess openings using online chess community data
Giordano De Marzo & Vito D. P. Servedio
Scientific Reports volume 13
Chess is a centuries-old game that continues to be widely played worldwide. Opening Theory is one of the pillars of chess and requires years of study to be mastered. In this paper, we use the games played in an online chess platform to exploit the “wisdom of the crowd” and answer questions traditionally tackled by chess experts. We first define a relatedness network of chess openings that quantifies how similar two openings are to play. Using this network, we identify communities of nodes corresponding to the most common opening choices and their mutual relationships.
Giordano De Marzo & Vito D. P. Servedio
Scientific Reports volume 13
Chess is a centuries-old game that continues to be widely played worldwide. Opening Theory is one of the pillars of chess and requires years of study to be mastered. In this paper, we use the games played in an online chess platform to exploit the “wisdom of the crowd” and answer questions traditionally tackled by chess experts. We first define a relatedness network of chess openings that quantifies how similar two openings are to play. Using this network, we identify communities of nodes corresponding to the most common opening choices and their mutual relationships.
