Expectimax Enhancement through Parallel Search for Non-Deterministic Games

Abstract

Expectimax, or expectiminimax, is a decision algorithm for artificial intelligence which utilizes game trees to determine the best possible moves for games which involves the element of chance. These are games that use a randomizing device such as a dice, playing cards, or roulettes. The existing algorithm for Expectimax only evaluates the game tree in a linear manner which makes the process slower and wastes time. The study intends to open new possibilities in game theory and game development. The study also seeks to make parallelism an option for enhancing algorithms not only limited to Artificial Intelligence. The objective of this study is to find a way to speed up the process of Expectimax which can eventually make it more efficient. The proponents used the game backgammon, written in Java, to apply Expectimax. The concept of parallel computing using thread pool is used to make the process of Expectimax faster. A game simulation between the existing Expectimax and the enhanced Expectimax is used as the test case for this study. After multiple test runs, the results showed that the enhanced Expectimax has chosen a move faster than the existing Expectimax 90% of the time. This shows that parallel computing speeds up the process of Expectimax. Index Terms—Expectimax, parallel search,