Generating interesting Monopoly boards from open data

Friberger, Marie Gustafsson; Togelius, Julian

doi:10.1109/cig.2012.6374168

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…Note that there is a distinction between a data intensive PCG system and a data-driven PCG system (though a system may indeed be both of these). A data-driven system uses external data to inform the generation of content; for example, a system to create monopoly boards based on real-world demographic data (Friberger and Togelius 2012). This content could then be generated using a dataintensive approach, with data filling in large templates created by a human, or a process-intensive approach, with the data being one of many components that informs the algorithm's decisions.…”

Section: Data Intensive To Process Intensive Systemsmentioning

confidence: 99%

The Future of Procedural Content Generation in Games

Smith

2021

AIIDE

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The future of procedural content generation (PCG) lies beyond the dominant motivations of “replayability” and creating large environments for players to explore. This paper explores both the past and potential future for PCG, identifying five major lenses through which we can view PCG and its role in a game: data vs. process intensiveness, the interactive extent of the content, who has control over the generator, how many players interact with it, and the aesthetic purpose for PCG being used in the game. Using these lenses, the paper proposes several new research directions for PCG that require both deep technical research and innovative game design.

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Section: Data Intensive To Process Intensive Systemsmentioning

confidence: 99%

The Future of Procedural Content Generation in Games

Smith

2021

AIIDE

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“…Dissemination of open data is important, since it can provide a substantial basis for argumentation in public speaking or policy making; in the context of education, it strengthens the connection between the concepts and facts taught at school with everyday life, and empowers students to make informed decisions in their life [27]. Friberger and Togelius [44] analyzed thoroughly the different kinds of information which can be used as properties in this context, from geographic data, which is typically readily available and easy to visualize, to demographic, which can be sensitive (in terms of politics). They mention that real-time infrastructure data, such as flight information, are becoming increasingly available and may be more appealing to avid game players since it helps them relate to the actual providers of the data.…”

Section: The Monopoly Game In Educationmentioning

confidence: 99%

Using Big and Open Data to Generate Content for an Educational Game to Increase Student Performance and Interest

Vargianniti¹,

Karpouzis

2020

BDCC

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The goal of this paper is to utilize available big and open data sets to create content for a board and a digital game and implement an educational environment to improve students’ familiarity with concepts and relations in the data and, in the process, academic performance and engagement. To this end, we used Wikipedia data to generate content for a Monopoly clone called Geopoly and designed a game-based learning experiment. Our research examines whether this game had any impact on the students’ performance, which is related to identifying implied ranking and grouping mechanisms in the game, whether performance is correlated with interest and whether performance differs across genders. Student performance and knowledge about the relationships contained in the data improved significantly after playing the game, while the positive correlation between student interest and performance illustrated the relationship between them. This was also verified by a digital version of the game, evaluated by the students during the COVID-19 pandemic; initial results revealed that students found the game more attractive and rewarding than a traditional geography lesson.

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“…Data games use freely available information to automatically generate game content [18]. This term was first proposed by Friberger and Togelius, describing a generator that transforms open data into Monopoly (Hasbro, 1935) boards [19]. Data games promote the visualization and interaction of information in creative and/or interesting ways.…”

Section: Data Gamesmentioning

confidence: 99%

DATA agent

Green

Barros

Liapis

et al. 2018

Proceedings of the 13th International Conference on the Foundations of Digital Games

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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.

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Generating interesting Monopoly boards from open data

Cited by 8 publications

References 11 publications

The Future of Procedural Content Generation in Games

The Future of Procedural Content Generation in Games

Using Big and Open Data to Generate Content for an Educational Game to Increase Student Performance and Interest

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