The Microsoft Kinect is the device that has promised to change the way we play games and interact with computers by making real time motion tracking possible on commodity hardware, but it’s potential doesn’t stop there. We’ve been exploring how it can massively expand the way players can customise their games.
Customisation is a big part of modern gaming, particularly in Massively Multiplayer Online games, where players customise their avatars to develop an individual identity within the game, and communicate that identity to other players. Up to now customisation has mostly been about changing how characters look, but that is only one aspect of what makes a character unique. How a character moves is also very important. Even more fundamentally we could customise how characters respond to events in the game, what game developers call Artificial Intelligence. Up to now customising these would involve complex animation and programming, skills that ordinary players don’t have. With Andrea Kleinsmith, I’ve been exploring how motion tracking like the kinect can make customising animtaion and AI easy. Players can use their own movements to make the animations for the characters. AI is harder, but we’ve been looking at how machine learning to build AI customisation tools. Rather than have to program the AI, players can act out examples of behavior using motion capture or a kinect, and our machine learning algorithms can infer AI rules to control the character.
We’ve recently had a paper published in the International Journal of Human-Computer Studies that describes a study we did that allowed players to customise thier avatars’ behaviour when they win or loose a point in a 3D version of the classic video game Pong. You can see it here:
Kleinsmith, Andrea and Gillies, Marco. 2013. Customizing by Doing for Responsive Video Game Characters. international journal of human-computer studies, 71(7), pp. 775-784. ISSN 1071-5819