2.0 – Player Behaviour Capture
Another use of machine learning in video games is for player modelling. Creating a model that represents one or more aspects of the player (playstyle, preferences, skill level, etc.) allows the game to adapt to the player, predict the players choices, represent the player in one or more ways among many others.
2.1 Drivatar™ Example
Forza Motorsport uses machine learning to model and reproduce player behaviour. This allows the computer to represent the player and their racing style in online games without the user being present (“Drivatar™ – Microsoft Research.”). The game AI uses a ‘racing line model‘ which is a smooth driving path through each segment of track (Graepel, Candela, and Herbrich, 2008). The player modelling process then uses information about how you navigate the turns in a track to train your Drivatar™. The variables used are related to: how consistently you drive; how smoothly you drive through corners; how you brake before a turn and consequently how you enter the turn; how quickly and accurately you navigate the apex of a corner; and how you exit the corner. The algorithm is trained from 5 races which represent a specific population sample of cars and tracks (“Drivatar™ in Forza Motorsport.”).
This is a good example of supervised learning in a video game.
2.2 Storytelling Example
Thue, Bultiko, Spetch, and Wasylishen did a very interesting study on the use of player modelling to create dynamic story lines based on individual preferences. They use the player types defined by of Robin D. Laws in his book Robin’s Laws of Good Game Mastering (2001) to evaluate their players. Whenever the player is faced with a decision, each outcome has defined player type association(s) which are used to update a genome which represents the current player’s preferences. The interactive story system then uses this genome to decide which events / plot points should come up next (Thue, David, Vadim Bultiko, Macia Spetch, and Eric Wasylishen, 2014).
This is another example of supervised learning. This implementation does not require training ahead of time, but collecting information about the player is required before accurate decisions can be made.
References
“Drivatar™ – Microsoft Research.” Drivatar™ – Microsoft Research. Microsoft Research, n.d. Web. 23 June 2014. <http://research.microsoft.com/en-us/projects/drivatar/>.
“Drivatar™ in Forza Motorsport.” Drivatar™ in Forza Motorsport. Microsoft Research, n.d. Web. 23 June 2014. <http://research.microsoft.com/en-us/projects/drivatar/forza.aspx>.
Graepel, Thore, Joaquin Quiñonero Candela, and Ralf Herbrich. “Machine Learning in Games: The Magic of Research in Microsoft Products.” . Microsoft Research Cambridge, 1 Jan. 2008. Web. 20 June 2014. <http://research.microsoft.com/en-us/events/2011summerschool/jqcandela2011.pdf>.
Thue, David, Vadim Bultiko, Macia Spetch, and Eric Wasylishen. “Interactive Storytelling: A Player Modelling Approach.” : n. pag. Association for the Advancement of Artificial Intelligence. Web. 23 June 2014.