Courses
Artificial Intelligence in Games
Understand the differences between traditional AI and AI applied to game development, where other factors such as playability are more relevant that the oponent’s intelligence level. Be familiar with the practical problems when developing AI for video games, and with the several techniques applied in comercial video games. Know how to design and build an AI system for a video game independently of its genre (action, sport, strategy, narrative).
Autonomous Agents and Multi-Agent Systems
To acquire general notions about agents and multi-agent systems; knowing how to identify and classify agents and environments, according to different properties. Knowing how to develop complex systems and systems from different application areas, using an agent-oriented methodology. Knowing how to define a society of agents in order to solve a specific problem. Being able to design agents with reactive, deliberative and hybrid architectures. Being able to create societies of agents that communicate, in a practical way, using suitable languages and platforms.
Computer Graphics for Games
This course covers both theory and practice of game engine software development. It delves into the different engine subsystems including, but not limited to, rendering, character animation, and physics, and details the articulation required to support gameplay development. By the end of this course, students should understand how modern game engines work, and be able to design and develop their own game engines.
Game Design
This course grants the students the opportunity to develop their skills on experience design and prototyping for games. The learning process is sustained in the discussion of what is a game, what are its components and what is its relation to the players (having in mind their differences). It is expected that the student develop design documents and prototypes to support his/her work on the course.
Game Development Methodology
Present a vision of the different methodologies and technologies involved in the development of digital games discussing the main features and issues in each one. Grant students with conceptual tools and techniques to develop user interfaces for games with special emphasis on player controls. Develop the ability to reflect and test the player experience and gameplay. Discuss the role of conceptual modelling and user testing. Highlight the importance to take a user centred approach in the exploration of the player experience.
Multimedia Content Production
Know the different types of multimédia information and how to manipulate them to poduce multimedia content. To understand the technological constraints that affect Production. To understand critical factors affect the success of a production, namely in aspects such as capture, encoding, processing and visualization of the different media. To know the different kinds of available authoring tools. To create Multimedia contents; To identify the different contexts in which multimedia can be consumed, with emphasys on online and network issues (evaluate bandwidth, latency, synchronization, etc.) and mobile devices. Introduce some advanged multimedia usages such as procedural modelling, generative art augmented reality. Apply efficient methods of multimedia content retrieval.
Thesis
inFlow: Adapting Gameplay to Player's Personality
In this document, we present a videogame that adapts its content to the player. Such a game needs to infer the player's type from his behavior, and then select how content is managed and presented to the player based on that type. In this work we focus on the later aspect, assuming we already know the player type. We also propose how such information can be used to enhance the player?s experience. After revising the literature on the subject, we decided to use the Demographic Game Design (DGD) model as our player model. Therefore, before playing our game, the player has to fill a questionnaire to assess his Myer-Briggs personality type. From this questionnaire, the game classifies the player according to the DGD model. The game is then adjusted according to this player type, which will influence how the information of the game is presented to the player, in three main aspects: presentation, difficulty management and depth of control over aspects of the game. To evaluate our approach, we asked different types of players to play our game under different conditions and evaluated the experience using a final questionnaire based on the GameFlow model. The evaluation suggests that the player enjoyment is higher when the game is using our framework to adapt to the player.
Co-creativity in Videogame Puzzle Creation
This work proposes a solution to improve the cooperation between humans and computer Artificial Intelligence (AI), as a colleague, in the creation of puzzles for video game levels. With this interaction we hope to give the designer a source of creative stimulus, in order to achieve overall more creative results than those obtained if said designer was working alone. The proposed solution consists of a co-creative puzzle creation tool, focused on improving creativity by allowing human and computer to work together in producing content using the Legend of Grimrock 2 Level Editor, exploring the digital “peer” paradigm. Its interface can be used by the designer to preview generated suggestions and orient its behavior. Suggestions are generated and iteratively evolved by three genetic algorithms and can be guided by the designer on different domains: objective, innovation, user map; all then combined in a fourth one that re-evaluates the best suggestions of each previous algorithm, again on the three domains, with different weights based on the users configuration, to choose the best suggestion overall. Results showed a positive influence on the puzzle creation because our approach takes into account the smaller nuances of the co-creative interaction. Outlined improvements such as a better way to support designer-specific interaction patterns, improved algorithm behaviour and integration with past tools set the direction for future work. We concluded that through an intuitive interface, flexible and adjustable behavior, we were able to provide some positive contributions to the quality of the co-creative puzzle creation process.