Modeling competitive game players with a positioning strategy in the great turtle race

Abstract

We propose a novel strategy of decision-making based on the idea of the position in which different players find themselves in a board game to focus not only on own piece but also on all pieces on the same position. This strategy will be independent of any particular search algorithm, thereby providing good quality movement for a general-purpose player. In an attempt to provide more insight into the nature of modeling artificial players three algorithms and five strategies in total have been implemented in the Great Turtle Race game. Based on statistical analysis the highest winning rate is found using this positioning strategy combined with alpha-beta pruning. In particular, this paper presents the joint model of these algorithms and strategies together with a concise summary of the game rules, suggesting possible correlations. These theoretical findings are complemented by experiments that were conducted to evaluate the winning rates.