Thesis

Creating believable social behaviour is a challenging task. We propose that imitating human players’ actions and having into account the influence of the players’ social identity improves the decision making process of an artificial agent, in terms of believability. To test this hypothesis, a predictive model was developed, based on a probabilistic approach to plan recognition using Dynamic Bayesian Networks (DBN) in both learning and testing phases. This work has been developed in the context of the INVITE game and aims to create an artificial agent described above and test if it is believable in games with human players.

Nowadays games allows its users to contribute in some way, shape or form. Little Big Planet is a platform video game for the Playstation 3 and consists of a sackboy that races through the various levels. This is one example of where users can create maps, customise the character (sackboy) and share this with other users. Users are no longer mere players and have the ability to create within the games. When one specifically individualises Educational games, user contribution may raise questions regarding the quality of the same. LudoPor is the tool for creating/playing/sharing Portuguese Language puzzle games used in this project. This tool is subject to a quality assessment at the time of being included on Facebook. Throughout this document we describe the implementation steps, including the quality control mechanisms (such as contents review). At the end, the results will be presented and the conclusions explained.

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.

Throughout the centuries education has evolved due to economical and cultural changes, leading to more complex requirements regarding societies needs. In the last decade gamification and incentive-centered design have been hot topics regarding the game and application industry in general. More recently, they have began being applied in medicine such as medical training, proper usage of medication, psychology evaluation and counselling. In the same context, the SIREN project aims to improve children's' conflict resolution capabilities in order to give them tools to better handle certain situations. This is done by giving them an open, friendly and safe environment to learn about conflict. The goal of this thesis is to improve a particular game of the SIREN project, "My Dream Theatre", making it an Intelligent Tutoring System. This is done with gamification techniques and procedural content generation, in order to better adapt the game during runtime and deliver a better and more personalized learning experience. The methodology applied to transform "My Dream Theatre" into an Intelligent Tutoring System and the conclusions and results drawn by this process are described in this document.