Knowledge Engineering

A vast amount of information about the real-world is available today in knowledge bases and open data repositories. My latest research revolves around extracting such real-world data and finding patterns which can be used to generate complete games.

DATA Agent

Michael Cerny Green, Gabriella A. B. Barros, Antonios Liapis and Julian Togelius

Abstract: This paper introduces DATA Agent, a system which creates murder mystery adventures from open data. In the game, the player takes on the role of a detective tasked with finding the culprit of a murder. All characters, places, and items in DATA Agent games are generated using open data as source content. The paper discusses the general game design and user interface of DATA Agent, and provides details on the generative algorithms which transform linked data into different game objects. Findings from a user study with 30 participants playing through two games of DATA Agent show that the game is easy and fun to play, and that the mysteries it generates are straightforward to solve.

in Proceedings of the 13th Conference on the Foundations of Digital Games, 2018. BibTex

Data-driven Design: A Case for Maximalist Game Design

Gabriella A. B. Barros, Michael Cerny Green, Antonios Liapis and Julian Togelius

Abstract: Maximalism in art refers to drawing on and combining multiple different sources for art creation, embracing the resulting collisions and heterogeneity. This paper discusses the use of maximalism in game design and particularly in data games, which are games that are generated partly based on open data. Using Data Adventures, a series of generators that create adventure games from data sources such as Wikipedia and OpenStreetMap, as a lens we explore several tradeoffs and issues in maximalist game design. This includes the tension between transformation and fidelity, between decorative and functional content, and legal and ethical issues resulting from this type of generativity. This paper sketches out the design space of maximalist data-driven games, a design space that is mostly unexplored.

in Proceedings of the International Conference of Computational Creativity, 2018. BibTex

Who Killed Albert Einstein? From Open Data to Murder Mystery Games

Gabriella A. B. Barros, Michael Cerny Green, Antonios Liapis and Julian Togelius

Abstract: This paper presents a framework for generating adventure games from open data. Focusing on the murder mystery type of adventure games, the generator is able to transform open data from Wikipedia articles, OpenStreetMap and images from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves around the murder of a person with a Wikipedia article, and populates the game with suspects who must be arrested by the player if guilty of the murder or absolved if innocent. Starting from only one person as the victim, an extensive generative pipeline finds suspects, their alibis, and paths connecting them from open data, transforms open data into cities, buildings, non-player characters, locks and keys and dialog options. The paper describes in detail each generative step, provides a specific playthrough of one WikiMystery where Albert Einstein is murdered, and evaluates the outcomes of games generated for the 100 most influential people of the 20th century.

IEEE Transactions on Games (accepted), 2018. BibTex

Game Character Ontology (GCO): A Vocabulary for Extracting and Describing Game Character Information from Web Content

Owen Sacco, Antonios Liapis, and Georgios N. Yannakakis

Abstract: Creating video games that are market competent costs in time, effort and resources which often cannot be afforded by small-medium enterprises, especially by independent game development studios. As most of the tasks involved in developing games are labour and creativity intensive, our vision is to reduce software development effort and enhance design creativity by automatically generating novel and semantically-enriched content for games from Web sources. In particular, this paper presents a vocabulary that defines detailed properties used for describing video game characters information extracted from sources such as fansites to create game character models. These character models could then be reused or merged to create new unconventional game characters.

In Proceedings of the International Conference on Semantic Systems, 2017. BibTex

A Holistic Approach for Semantic-Based Game Generation

Owen Sacco, Antonios Liapis and Georgios N. Yannakakis

Abstract: The Web contains vast sources of content that could be reused to reduce the development time and effort to create games. However, most Web content is unstructured and lacks meaning for machines to be able to process and infer new knowledge. The Web of Data is a term used to describe a trend for publishing and interlinking previously disconnected datasets on the Web in order to make them more valuable and useful as a whole. In this paper, we describe an innovative approach that exploits Semantic Web technologies to automatically generate games by reusing Web content. Existing work on automatic game content generation through algorithmic means focuses primarily on a set of parameters within constrained game design spaces such as terrains or game levels, but does not harness the potential of already existing content on the Web for game generation. We instead propose a holistic and more generally-applicable game generation solution that would identify suitable Web information sources and enrich game content with semantic meta-structures.

in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG). 2016. BibTex

Murder Mystery Generation from Open Data

Gabriella A. B. Barros, Antonios Liapis and Julian Togelius

Abstract: This paper describes a system for generating murder mysteries for adventure games, using associations between real-world people mined from Wikipedia articles. A game is seeded with a real-world person, and the game discovers suitable suspects for the murder of a game character instantiated from that person. Moreover, the game discovers characteristics of the suspects which can act as clues for the player to narrow down her search for the killer. The possible suspects and their characteristics are collected from Wikipedia articles and their linked data, while the best combination of suspects and characteristics for a murder mystery is found via evolutionary search. The paper includes an example murder mystery generated by the system revolving around the (hypothetical) death of a contemporary celebrity.

in Proceedings of the International Conference on Computational Creativity. 2016. BibTex

Playing with Data: Procedural Generation of Adventures from Open Data

Gabriella A. B. Barros, Antonios Liapis and Julian Togelius

Abstract: This paper investigates how to generate simple adventure games using open data. We present a system that creates a plot for the player to follow based on associations between Wikipedia articles which link two given topics (in this case people) together. The Wikipedia articles are transformed into game objects (locations, NPCs and items) via constructive algorithms that also rely on geographical information from OpenStreetMaps and visual content from Wikimedia Commons. The different game objects generated in this fashion are linked together via clues which point to one another, while additional false clues and dead ends are added to increase the exploration value of the final adventure game. This information is presented to the user via a set of game screens and images. Inspired by the "Where in the World is Carmen Sandiego?" adventure game, the end result is a generator of chains of followable clues.

in Proceedings of the International Joint Conference of DiGRA and FDG. 2016. BibTex

Data Adventures

Gabriella A. B. Barros, Antonios Liapis, Julian Togelius

Abstract: This paper outlines a system for generating adventure games based on open data, and describes a sketch of the system implementation at its current state. The adventure game genre has been popular for a long time and differs significantly in design priorities from game genres which are commonly addressed in PCG research. In order to create believable and engaging content, we use data from DBpedia to generate the game's non-playable characters locations and plot, and OpenStreetMaps to create the game's levels.

in Proceedings of the FDG workshop on Procedural Content Generation in Games, 2015. BibTex