Presentation information

Poster presentation

General Session » Interactive

[3Pin1] インタラクティブ(1)

Thu. Jun 7, 2018 9:00 AM - 10:40 AM Room P (4F Emerald Lobby)

9:00 AM - 10:40 AM

[3Pin1-34] An AI game player trained by a genetic algorithm that avoids bombardments in a shooting game

〇Shizuma Namekawa1, Taro Tezuka1 (1. University of Tsukuba)

Keywords:genetic algorithm, shooting game, game AI

Making programs play games has contributed significantly to the advancement of AI research. It is partly because popular games often resemble real-world problems. Tackling them has enabled AI to cope with real-world scenarios as well. Now that AI has surpassed humans in strategy board games such as chess and go, one next target would be to train it to play video games. This paper focuses on a shooting game and optimizes a program to avoid bombardments deployed by the opposing player. Using a genetic algorithm, the AI player was optimized to move around without hitting enemy attacks. The results of experiments showed that it can successfully learn to do so, although with much computation time.