Enhancing Artificial Intelligence on a Real Mobile Game

摘要

Mobile games represent a killer application that is attracting millions of subscribers worldwide.One of the aspects crucial to the commercial success of a game is ensuring an appropriatelychallenging artificial intelligence (AI) algorithm against which to play. However, creating thiscomponent is particularly complex as classic search AI algorithms cannot be employed by limiteddevices such as mobile phones or, even on more powerful computers, when considering imperfectinformation games (i.e., games in which participants do not a complete knowledge of the gamestate at any moment). In this paper, we propose to solve this issue by resorting to a machinelearning algorithm which uses profiling functionalities in order to infer the missing information,thus making the AI able to efficiently adapt its strategies to the human opponent. We studied asimple and computationally light machine learning method that can be employed with success,enabling AI improvements for imperfect information games even on mobile phones. We created amobile phone-based version of a game calledGhostsand present results which clearly show theability of our algorithm to quickly improve its own predictive performance as far as the number ofgames against the same human opponent increases.