Inference of Opponent’s Uncertain States in Ghosts Game Using Machine Learning

Abstract

Among many categories, board games can be classified into two main categories: Games with perfect information and games with imperfect information. The first category can be represented by the example of "Chess" game where the information about the board is open to both players. The second category can be determined with the "Ghosts" game. Players can see the position of the opponent's pieces on the board whereas the identity of the ghost pieces (good or bad) is hidden, which makes this game uncertain to apply search state space based technique. In this work, we have investigated the opponent game state with uncertainty for Ghosts using machine learning algorithms. From last year competition replay data, we extracted several features and apply various machine learning algorithms to infer game state. Also, we compare our experimental results to the previous prototype based approach. As a result, our proposed method shows more accurate results.