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

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.

Orchestrating Game Generation

“…Similar objectives can be tailored through a graphical user interface as a target hue selected by the user on a color wheel [11], less directly as an intended tension curve [15], or inferred based on player interactions with generated results [44]. Human input can also take the form of English text: A Rogue Dream [45] and WikiMystery [46] require a single word or a person's name as input, respectively, to draw inspiration from. Extensive human authoring may also be required: Game-O-Matic [47] requires a user-created graph with customized edges and nodes, while mission graphs in Dwarf Quest must similarly be hand-authored along with their node types [48].…”

“…While most of the instances of multifaceted generators are based on content generated from scratch, this does not have to be the case. The Data Adventures series [46], [55], [91] create simple adventure games based on components already existing and freely available as open access data. Using primary sources of open content such as Wikpedia for data, Wikimedia Commons for images, and OpenStreetMap for levels, Data Fig.…”

“…Adventures recombine that information in novel (and often absurd) ways to create adventures for finding an NPC specified at the start of the game [55] or for discovering the culprit of a murder [46], [91]. The generative system finds links between people, places, and topics in Wikipedia and crafts a series of encounters with different "real" people (i.e., existing in Wikipedia) in locations around the globe, the maps of which are collected from OpenStreetMap; finally, the images of places and people are collected from Wikimedia Commons.…”

Towards Semantic Digital Games for Semantic Digital Libraries

“…We had also developed a light-weight vocabulary for describing character models (Sacco et al 2017) and also created a light-weight vocabulary for linking in-game events and entities to social data (Sacco et al 2012). Moreover, similar work in Green et al (2019) and Barros et al (2018) demonstrate how to create mystery adventure games from open data, mainly from DBpedia 9 datasets which contain semantically enriched content extracted from Wikipedia 10 . However, both in our previous work and the current state of the art have not examined how semantic digital games can be used for semantic digital libraries and focused on using DBpedia rather than harvesting more linked-data and open datasets.…”

Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation

“…Thus far, the focus has been on discussing PCG and presenting algorithms that create content mostly autonomously. Automated game design is a complex task since it is required to create content (or full games) by itself with the help of heuristics, user models, and logic among the content created [108][109][110][111]. However, another paradigm within PCG is the mixed-initiative paradigm, where AI can collaborate with a designer to co-design games.…”