Options
2008
Conference Paper
Titel
Towards engaging games
Abstract
One of the challenges that a computer game developer meets when creating a new game is setting the difficulty ``right''. Not only is it a complicated task to balance all the game parameters, but the developer also needs to cater for players with very different skill levels. Providing a game with an ability to automatically scale the difficulty depending on the current player would make the games more engaging over longer time. While a few commercial games boast about having such a system, to the best of our knowledge treating it as a machine learning problem has received surprisingly low attention. In this paper, we investigate the {\it automatic difficulty scaling problem} from a more general perspective: How can agents learn to keep agents engaged. After introducing the learning problem, called \emph{Engagement Problem}, we outline a framework based on nested Markov Decision Processes, called \emph{Engaging Markov Decision Process}, for solving it. Preliminary experiments in a small grid world show the effectiveness of our approach.
Konferenz