Position: Ph.D. student

Current Institution: New York University

Procedural Game Content Generation from Open Data

Users upload data to the Internet perpetually. From Wikipedia articles to online news, from Youtube videos to Twitter messages, the sheer diversity of available information is immense, reflecting the real world in 1’s and 0’s. Data games make use of freely available online data to automatically generate game content. In such games, the players should view, interact with and learn from the original data during gameplay. They provide novel and playful ways of understanding real world information that may otherwise be tedious, difficult or overwhelming, due to its complexity, size or even low entertainment value. For example, a user may play an action game where the map reflects his neighborhood, or a strategy game where attributes are based on a country’s demographic information. Thus, data games have the potential of serving as visualization tools for open data, while also providing new inspiration sources for content generation.

Transforming open data into game content is not trivial, for data in its raw form is unsuited for direct use in-game. Once data is acquired, the transformation process is divided into data selection and structural transformation. The former involves selecting data parts useful to content generation, while the latter includes adapting the selected data to fit the desired content. My research aims at expanding the concept of data games, exploring how different types of data can be transformed into different kinds of playable content with artificial intelligence, and how the original data is perceived by the player during gameplay. I developed a model for procedural content generation for data games, and aim at applying it in the development of various game prototypes. An initial prototype used geographical information for map generation in an open-source version of the classic strategy game Civilization (MPS Labs, 1991), while a prototype in development attempts at using Wikipedia articles to generate themed cards and decks for the game Hearthstone (Blizzard Entertainment, 2014). Currently, I am developing Data Adventures, a murder-mystery adventure game that uses Wikipedia articles to generate the whole game, including its plot, characters, dialogue, items, in-game locations and images. Although Data Adventures is a work-in-progress, it is fully playable, providing interaction with non-playable characters based on real people and locations based on real-world places.

Gabriella A. B. Barros was awarded a scholarship from Science Without Borders and CAPES topursue her PhD, initially at the IT University of Copenhagen (Denmark). She is currently a PhDstudent at the Tandon School of Engineering of the New York University (US), advised by JulianTogelius. She holds a B.Sc. in Computer Science from the Federal University of Alagoas(Brazil), and a M.Sc. in Computer Science from the Federal University of Pernambuco (Brazil).Her main research focus is Data Games, which are games with procedurally generated contentbased upon open data. Additional interests are procedural content generation and artificialintelligence.