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from the abstract: "This thesis identifies the need for an advanced search function which takes into consideration the notions of purpose and contextual circumstance of using educational games in order for such a database to be of greater usefulness for users. This thesis presents a design of such a search function, based on the theories of Purushotma (2005), Pannese and Carlesi (2007), Charsky (2010) and Reinders and Wattana (2011). Furthermore this thesis provides an updated metadata model to support such a search function. In the future the search function could be polished from a usability perspective and further developed to incorporate other types of serious games."

Abstract :"We present an attempt at exploring the search space of weapons in team-based multiplayer First-Person Shooters (FPS). At the foundation of the experiment is Team Block- head Wars (TBHW), a game that we developed for the pur- poses of this project. TBHW allows human players to enjoy classic multiplayer FPS gameplay and uses a genetic algo- rithm to continuously generate new weapons. A weapon's genome consists of ten real-valued parameters, which to- gether form a vast search space that includes common FPS weapon tropes. The evaluation function scores weapons on the basis of their use by players. The game also generates 3D meshes to visually represent the generated weapons for easy player recognition. While TBHW is work in progress, preliminary results are encouraging."

Abstract: "This paper introduces Rank-based Interactive Evo- lution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of- the-art interactive evolution approaches"