Data Adventures

This paper outlines a system for generating adventure games based on open data, and describes a sketch of the system im-plementation 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 ad-dressed 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. 1

Generating Levels That Teach Mechanics

The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.Comment: 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International Workshop on Procedural Content Generation (PCG2018

Playing with data : procedural generation of adventures from open data

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.peer-reviewe

Data adventures

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 signi cantly 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.peer-reviewe

Data-driven design : a case for maximalist game design

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 Open- StreetMap, 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.peer-reviewe

Data-driven Design: A Case for Maximalist Game Design

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.Comment: 9 pages, 2 Figures, Accepted in ICCC 201

DATA Agent

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.peer-reviewe