DATA agent

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

doi:10.1145/3235765.3235792

Cited by 12 publications

(9 citation statements)

References 14 publications

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“…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.…”

Section: Data Adventuresmentioning

confidence: 99%

“…Level design is based on the real location of cities on the globe, while city layouts are based on OpenStreetMap. In-game screenshot is from [91]. Fig.…”

Section: Data Adventuresmentioning

confidence: 99%

“…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.…”

Section: Data Adventuresmentioning

confidence: 99%

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Orchestrating Game Generation

et al. 2019

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The design process is often characterized by and realized through the iterative steps of evaluation and refinement. When the process is based on a single creative domain such as visual art or audio production, designers primarily take inspiration from work within their domain and refine it based on their own intuitions or feedback from an audience of experts from within the same domain. What happens, however, when the creative process involves more than one creative domain such as in a digital game? How should the different domains influence each other so that the final outcome achieves a harmonized and fruitful communication across domains? How can a computational process orchestrate the various computational creators of the corresponding domains so that the final game has the desired functional and aesthetic characteristics? To address these questions, this paper identifies game facet orchestration as the central challenge for artificial-intelligence-based game generation, discusses its dimensions, and reviews research in automated game generation that has aimed to tackle it. In particular, we identify the different creative facets of games, propose how orchestration can be facilitated in a top-down or bottom-up fashion, review indicative preliminary examples of orchestration, and conclude by discussing the open questions and challenges ahead.

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Section: Data Adventuresmentioning

confidence: 99%

“…Level design is based on the real location of cities on the globe, while city layouts are based on OpenStreetMap. In-game screenshot is from [91]. Fig.…”

Section: Data Adventuresmentioning

confidence: 99%

Section: Data Adventuresmentioning

confidence: 99%

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Orchestrating Game Generation

et al. 2019

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“…Sandbox simulation games as mentioned earlier also taken an advantage by using PCG to generate player avatar as sandbox simulation games able to create countless amount variations [99]. As well as storybased simulation games to generate its dialog and story by using PCG [39].…”

Section: Game Modelmentioning

confidence: 99%

A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation

Hafis

Tolle

Supianto

2019

JITeCS

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Procedural Content Generation (PCG) is an emerging field of study in computer science that focuses on automating the process of generating content by using algorithm, making the content generation process with less human effort. However, a more specific empirical evidence on how it is being used in a game-related implementation are still lacking. This paper presents the findings of review performed in the past 5 years looking on how PCG are being applied in game-related content, whether it is from the basic paper characteristic to analyze the trends, the field of PCG itself, and the game domain of game model and game genre. The studies had shown that PCG are being used extensively in game-related content but has seen more uses on specific type of contents rather than being used ubiquitously in all sorts of contents. The result shown that there are not specific best type of PCG method or algorithm being used instead an array of approach can be used based on what content being created. Result also shown that PCG are being used in multiple type of games, but similarly, based on the paper found, only certain types of game benefits PCG extensively such as action and platforming games while other model and genre of games have not seen much PCG application yet. Further studies are also required to analyze how experimentation and evaluation of PCG are being done as well as PCG domain in educational games as well as game-based learning, the quality catachrestic being analyzed on the papers are also worth mentioning to understand the underlying result of PCG usage in game-related contents.

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“…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.…”

mentioning

confidence: 99%