Games with Dynamic Difficulty Adjustment using POMDPs

In this paper the approach of using a partially observable Markov model for games with dynamical difficulty adjustment is introduced. This approach leads implicitly to a strategy which balances gathering information about the player through his or her behavior with adjusting the game to the estimated player’s abilities and preferences. We will show how this approach can be used in a stroke rehabilitation system, where a person plays a game in which the controller is a rehabilitation device. We show that the parameters of the model have a clear influence on the behavior of the system and that aspects of the player’s abilities and characteristics can be measured by observing the behavior.