Abstract
Videogames have become extremely expensive to produce. In order to please all kinds of players, developers have turned to industry-proven genres. This has left the market overfilled with similar games and many potential players unengaged and uninterested. Recent work in the field of entertainment modelling has successfully developed methods that can increase engagement and play-time for different types of players, allowing games to reach a broader audience. This work addresses the problem of recognizing player personality as a first step in entertainment modelling. We describe our solution that is comprised of a task-based scenario and a Bayesian network system that classifies personality based on sample data from the scenario. We also extensively describe our methodology for developing the task-based scenario, from initial task collection to integration with the Bayesian network system. This methodology, based on the Keirsey Temperament Model, was developed to be ported and applied over different games, genres and player models. To prove the usefulness of our methodology, we applied it to develop a concrete scenario for the game Minecraft and tied it to an infering system. We then validated our concrete solution by running several cross-validation tests over our system to achieve sucess rates. We have thus successfully developed the first part of an adaptive system, one that can identify personality and interests to adapt content and increase entertainment in games.