3.0 – ML in Multiplayer Environments
3.1 – Player Modelling
In my previous post I discussed the uses of player modelling in the Forza Motorsport series. Capturing player behaviour allows online games to simulate multiplayer gameplay without the need for both players to be actively involved at the same time. Musick, Bowling, Furnkraz, and Graepel also mention the potential for this technology in MMORPGs (Massively Multiplayer Online Role-Playing Games) where a large persistent universe may require a character to be present regardless of the availability of their human counter-part (Musick, Bowling, Furnkraz, and Graepel, 2006).
3.2 – Match-making
Machine learning has great potential in online match-making. As discussed in Part 1, players enjoy themselves the most when an enemy AI can create just enough challenge to match their skill level and play style. The same ideas hold true for online play, and it become a significant challenge to create a system which can match players and teams together in a way that is satisfying for all.
Xbox live employs a matchmaking system called TrueSkill™. The system addresses some of the major challenges in mutliplayer match making systems. These include the idea that team-based match outcomes need to provide skill information about each individual player, and that estimating the skill level of a team requires knowledge of each members skill and how they may act together. The math may be a little beyond me, but essentially the learning part of TrueSkill™ is when it deals with a player’s skill level. The system uses outcome of a game along with each players estimated performance within their team top update the player’s recorded skill level. TrueSkill™can then use the skill levels for each player to create increasingly more even, fair matches that are enjoyable to all members (Herbrich, Minka, and Graepel, 2006).
Musick, Ron, Michael Bowling, Johnannes Furnkranz, and Thore Graepel. “Machine learning and games.” Machine Learning 63: 211-215. SpringerLink. Web. 25 June 2014.
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>.
Herbrich, Ralf, Tom Minka, and Thore Graepel. “Trueskill™: A Bayesian skill rating system.” Advances in Neural Information Processing Systems. 2006.