Online adaptive controller for simulated car racing

Abstract

An adaptive game AI has the potential of tailoring a uniquely entertaining and meaningful game experience to a specific player. An online adaptive AI should be able to profile its opponent efficiently during the early phase of the game and adapts its own playing style to the level of the player so that the player feels entertained playing against it. This paper presents an online adaptive algorithm that uses ideas from evolutionary computation to match the skill level of the opponent during the game. The proposed algorithms demonstrated using a car racing simulator is capable of matching its opponents in terms of both mean score and winning percentages. © 2008 IEEE.