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Can intracranial vessel wall MR imaging help make high risk procedures safer? [PDF]

J Neurointerv Surg
Chatterjee AR.
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Procedural Content Generation

2019
Данная статья представляет краткую историю процедурной генерации контента и разъясняет общее значение этого процесса. За описанием ключевых событий и разработок в данной сфере следуют определения термина «процедурная генерация контента», данные исследователями в данной области.
Korda, S. O., Kovaleva, A. G.
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Compositional procedural content generation

Proceedings of the The third workshop on Procedural Content Generation in Games, 2012
We consider the strengths and drawbacks of various procedural content generation methods, and how they could be combined to hybrid methods that retain the advantages and avoid the disadvantages of their constituent methods. One answer is composition, where one method is nestled inside another. As an example, we present a hybrid evolutionary-ASP dungeon
Togelius, Julian, Justinussen, Tróndur, Hartzen, Anders   +2 more
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Procedural content generation for games

ACM Transactions on Multimedia Computing, Communications, and Applications, 2013
Hundreds of millions of people play computer games every day. For them, game content—from 3D objects to abstract puzzles—plays a major entertainment role. Manual labor has so far ensured that the quality and quantity of game content matched the demands of the playing community, but is facing new scalability challenges due to the exponential growth over
Mark Hendrikx, Sebastiaan Meijer, Joeri Van Der Velden, Alexandru Iosup   +3 more
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Procedural Content Generation

2016
Procedural content generation is a technique still used in modern games, but more generally there are modern non-game situations where (possibly but not necessarily repeatable) variety is required, and the results cannot be simply precomputed and stored.
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Understanding procedural content generation

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2014
Games that use procedural content generation (PCG) do so in a wide variety of ways and for different reasons. One of the most common reasons cited by PCG system creators and game designers is improving replayability by providing a means for automatically creating near-infinite amounts of content, the player can come back and replay the game and refine ...
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Markov Models for Procedural Content Generation

2021
Procedural content generation (PCG) is a growing area of research focused on leveraging artificial intelligence in the design and creation of content (e.g., levels, environments, stories, etc.) oftentimes for video games. However, most current PCG approaches are domain specific or require a substantial amount of domain knowledge to be used across ...
Sam Snodgrass, Santiago Ontañón
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Designing Games with Procedural Content Generation

Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, 2015
This paper describes the design of a novel approach to procedural content generation, intent on supporting game design activities. The distinctive factor in this approach is that content generation is guided by a series of target experience indicators, which the designer can define freely according to his own agenda.
Rui Craveirinha, Licinio Roque
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Procedural Content Generation in the Game Industry

2017
Game content construction and generation are laborious and expensive. Procedural content generation (PCG) aims at generating game content automatically using algorithms, reducing the cost of game design and development. PCG systems have the potential to act as “on-demand game designers,” but need to be as flexible as possible while creating content ...
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Customizable Procedural Content Generation with LLMs

Anais do XXIV Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames 2025)
Introduction: Procedural Content Generation (PCG) faces challenges in creating customizable levels with specific characteristics, such as difficulty patterns and unique layouts. Large Language Models (LLMs) offer a promising solution due to their natural language understanding and generative capabilities.
Marcelo Júnior, Mario Adaniya, Luiz Nunes   +2 more
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