Capturing and Enhancing Player Entertainment in Games (CEPEG), Using Entertainment Modeling and Adaptive Learning

Aims and Objectives

The research goals of this 2-year project are to model in real time computer game players/users satisfaction and strategies for commercial games using information available from the game platform, and to construct software able to do this.

To achieve these goals, we plan to:

1. devise a method that efficiently identifies qualitative features contributing to player satisfaction;

2. design quantitative measures for these features;

3. investigate the correlation between the type of player, the real-time satisfaction estimation and the quantitative feature values and

4. develop and implement AI techniques based on user modelling and machine learning that will augment the entertainment value of the game in real-time.

The key research questions here are first, whether the techniques for measuring player interest which have previously developed for simple games will scale to commercial; second, whether those scaled techniques can support control and learning methods for increasing player satisfaction, and third, whether entertainment modelling and adaptive learning will increase the motivation of the user for deep learning in learning environments that use games.

Last updated: 22/02/07