In this project, a Monte Carlo tree search player was designed and implemented for the child’s game dots and boxes, the computational burden of which has left traditional artificial intelligence approaches like minimax ineffective. Two potential improvements to this player were implemented using game-specific information about dots and boxes: the lack of information for decision-making provided by the net score and the inherent symmetry in many states. The results of these two approaches are presented, along with details about the design of the Monte Carlo tree search player. The first improvement, removing net score from the state information, was proven to be beneficial to both learning speed and memory requirements, while the second, accounting for symmetry in the state space, decreased memory requirements, but at the cost of learning speed.
Advisor(s) or Committee Chair
Dr. Uta Ziegler
Computer Sciences | Game Design | Theory and Algorithms
Prince, Jared, "Game Specific Approaches to Monte Carlo Tree Search for Dots and Boxes" (2017). Honors College Capstone Experience/Thesis Projects. Paper 701.