Enhanced Naive Agent in Angry Birds AI Competition via Exploitation-Oriented Learning

The Angry Birds AI Competition engages artificial intelligence agents in a contest based on the game Angry Birds. This tournament has been conducted annually since 2012, with participants competing for high scores. The organizers of this competition provide a basic agent, termed “Naive Agent,” as a baseline indicator. This study enhanced the Naive Agent by integrating a profit-sharing approach known as exploitation-oriented learning, which is a type of experience-enhanced learning. The effectiveness of this method was substantiated through numerical experiments. Additionally, this study explored the use of level selection learning within a multi-agent environment and validated the utility of the rationality theorem concerning the indirect rewards in this environment.