Title :
Extracting NPC behavior from computer games using computer vision and machine learning techniques
Author :
Fink, Alex ; Denzinger, Jörg ; Aycock, John
Author_Institution :
Dept. of Math., California Univ., Berkeley, CA
Abstract :
We present a first application of a general approach to learn the behavior of NPCs (and other entities) in a game from observing just the graphical output of the game during game play. This allows some understanding of what a human player might be able to learn during game play. The approach uses object tracking and situation-action pairs with the nearest-neighbor rule. For the game of Pong, we were able to predict the correct behavior of the computer controlled components approximately 9 out of 10 times, even if we keep the usage of knowledge about the game (beyond observing the images) at a minimum
Keywords :
computer games; computer vision; learning (artificial intelligence); object detection; tracking; NPC behavior; agent modeling; computer game; computer vision; game play; human player; machine learning; nearest-neighbor rule; nonplayer characters; object tracking; situation-action pairs; Application software; Computational intelligence; Computer displays; Computer science; Computer vision; Humans; Machine learning; Mathematics; Object recognition; Physics; Agent Modeling; Computer Games; Object Tracking;
Conference_Titel :
Computational Intelligence and Games, 2007. CIG 2007. IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0709-5
DOI :
10.1109/CIG.2007.368075
