2018/03/15 by Didier Barradas‐Bautista, Barradas-Bautista, Didier, Matı́as Alvarado +1
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Applied Physics (physics.app-ph) #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Data Analysis #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #FOS: Physical sciences #Opinion Dynamics and Social Influence #Reinforcement Learning in Robotics #Sports Analytics and Performance #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1803.05983
openalex publication_date 2018/03/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Go gaming is a struggle between adversaries, black and white simple stones,\nand aim to control the most Go board territory for success. Rules are simple\nbut Go game fighting is highly intricate. Stones placement and interaction on\nboard is random-appearance, likewise interaction phenomena among basic elements\nin physics thermodynamics, chemistry, biology, or social issues. We model the\nGo game dynamic employing an Ising model energy function, whose interaction\ncoefficients reflect the application of rules and tactics to build long-term\nstrategies. At any step of the game, the energy function of the model assesses\nthe control and strength of a player over the board. A close fit between\npredictions of the model with actual game's scores is obtained. AlphaGo\ncomputer is the current top Go player, but its behavior does not wholly reveal\nthe Go gaming nature. The Ising function allows for precisely model the\nstochastic evolutions of Go gaming patterns, so, to advance the understanding\non Go own-dynamic -beyond the player`s abilities. The analysis of the frequency\nand combination of tactics shows the formation of patterns in the groups of\nstones during a game, regarding the turn of each player, or if human or\ncomputer adversaries are confronted.\n