Exploring optimization strategies in board game Abalone for Alpha-Beta search

Abstract

This paper discusses the design and implementation of a highly efficient MiniMax algorithm for the game Abalone. For perfect information games with relatively low branching factor for their decision tree (such as Chess, Checkers etc.) and a highly accurate evaluation function, Alpha-Beta search proved to be far more efficient than Monte Carlo Tree Search. In recent years many new techniques have been developed to improve the efficiency of the Alpha-Beta tree, applied to a variety of scientific fields. This paper explores several techniques for increasing the efficiency of Alpha-Beta Search on the board game of Abalone while introducing some new innovative techniques that proved to be very effective. The main idea behind them is the incorporation of probabilistic features to the otherwise deterministic Alpha-Beta search.