Learning Believable Player Movement Patterns from Human Data in a Soccer Game
説明
Player movement patterns are one of the behavioral traits immediately visible to an observer. Thus, a soccer AI system striving for believable (human-like) behavior must ensure the believability of player movements. We show how tracking data of real human players in soccer can be used to create a case-based reasoning AI system, able to simulate realistic player movements in a computer soccer game. Our results are confirmed with a direct comparison of actions made by AI-controlled players and professional athletes.
収録刊行物
-
- 2020 22nd International Conference on Advanced Communication Technology (ICACT)
-
2020 22nd International Conference on Advanced Communication Technology (ICACT) 91-93, 2020-02-01
IEEE