Thesis

Children are usually the age group that is most affected by natural catastrophes. Therefore it is extremely important to educate them on what they need to do in case of danger to protect themselves. To achieve this goal appeared Treme-Treme, a serious game that endeavors to educate school-age children on how to behave before, during, and after the occurrence of an earthquake and/or a tsunami. The game was first developed in 2014 in a partnership between the Departments of Computer Science and Engineering and Civil Engineering for a European project on Urban Disaster Prevention Strategies using Macroseismic Fields and Fault Sources. As the game continued to be used, it became evident, in early 2018, that some changes were needed, not only to keep up with the new web and mobile technologies but also to improve some of the existing features. However, this restructuring has not concluded, and since this game is still active in multiple schools, it was important to complete all the changes to provide all the elements for the students. Thus, this work comes as a follow-up to the previous one, with the missing levels being migrated to the new platforms and making several changes to the structure and functioning of the pre-existing game. In addition to completing the restructuring of the game, this work also resulted in a remote monitoring mechanism of the performance that provides researchers/project managers with information on both the game and the player, both nationally and internationally.

Pic-a-boo is multiplayer game about taking pictures of other players in a dark room. Not all information is available at all time to the players, so some guessing is required to win. In this work, we developed artificial agents that would be able to imitate human players and serve as their substitute when needed. This was achieved through an artificial neural network iteratively trained by playing agains a human player, in the attempt of capturing and replicating what makes his or her gameplay distinct and identifiable as a player. Although the agents were able to navigate the map believably, other actions such as taking photos were produced randomly without any discernible human pattern.

Children that have problems in developing socialization skills before their teens can become socially isolated, leading to low self-esteem and social alienation, and possibly snowballing into more serious psychological problems further into adulthood. In this thesis we present a the implementation for a Serious Game, LINA, that uses Contact Theory to help pre-adolescent children improving the relations with their peers. In LINA, the players, children from 10 to 12 years old, will try to find out what happened to a missing colleague - Lina - and her story through the discovery of augmented reality clues and overcoming challenges cooperatively. This document specifies the game concept, methodology and implementation of a digital prototype for demonstration. Also evaluates said prototype regarding its usability, enjoyment and interest for the players. Conducted evaluation determined that the players find the gameplay and story fun and are keen on playing more of the game. Future work involves revising this first prototype with the feedback from the evaluation session, coming up with new challenges and exploring technical limitations of Augmented Reality in the game context.

Challenges in video games tend to be created in a ’one challenge for all players’ fashion, which creates different experiences for different players given that they do not all possess the same skills required to overcome said challenges. Some games offer the choice between a few difficulty settings like the well known easy, normal and hard format. But what if instead of one difficulty for everyone or making the player choose the difficulty he wants, the game could adjust its challenges to suit each player in a way that would make the experience felt by all players similar? Based on a previous work that proved that, for the game used, such a model increased both time spent playing and number of times the game was replayed, when comparing an adaptive model with a conventional way of creating challenges, the work presented in this document investigated how the adaptation of the challenges affected the experience of the player where both versions of the game used the same method to create the challenges, but where one of them would adapt the challenges to the player and the other would not. The conclusions were that there was no significant difference in time played nor in how many times the game was replayed, but the challenge felt by the players of the adaptive version was more homogeneous, meaning the challenge felt by testers was very similar, and that no loss of competence was felt by these players when compared to the non-adaptive version.