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of 33
pro vyhledávání: '"Snodgrass, Sam"'
Autor:
Berns, Sebastian, Volz, Vanessa, Tokarchuk, Laurissa, Snodgrass, Sam, Guckelsberger, Christian
Similarity estimation is essential for many game AI applications, from the procedural generation of distinct assets to automated exploration with game-playing agents. While similarity metrics often substitute human evaluation, their alignment with ou
Externí odkaz:
http://arxiv.org/abs/2402.18728
Autor:
Sarkar, Anurag, Guzdial, Matthew, Snodgrass, Sam, Summerville, Adam, Machado, Tiago, Smith, Gillian
Publikováno v:
Sarkar, Anurag, et al. "Procedural Content Generation via Knowledge Transformation (PCG-KT)." IEEE Transactions on Games (2023)
We introduce the concept of Procedural Content Generation via Knowledge Transformation (PCG-KT), a new lens and framework for characterizing PCG methods and approaches in which content generation is enabled by the process of knowledge transformation
Externí odkaz:
http://arxiv.org/abs/2305.00644
Autor:
Liu, Jialin, Snodgrass, Sam, Khalifa, Ahmed, Risi, Sebastian, Yannakakis, Georgios N., Togelius, Julian
Publikováno v:
Neural Computing and Applications 2020 (Early Access)
Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based methods have been applied to various content types such as levels, maps,
Externí odkaz:
http://arxiv.org/abs/2010.04548
Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training, recent works h
Externí odkaz:
http://arxiv.org/abs/2009.06356
Autor:
Snodgrass, Sam, Sarkar, Anurag
Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. An important facet of creativity is combinational creativit
Externí odkaz:
http://arxiv.org/abs/2006.09807
Autor:
Volz, Vanessa, Justesen, Niels, Snodgrass, Sam, Asadi, Sahar, Purmonen, Sami, Holmgård, Christoffer, Togelius, Julian, Risi, Sebastian
Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are able to generate content for different games (e.g. Super Mario Bros.,
Externí odkaz:
http://arxiv.org/abs/2005.12579
Autor:
Summerville, Adam, Snodgrass, Sam, Guzdial, Matthew, Holmgård, Christoffer, Hoover, Amy K., Isaksen, Aaron, Nealen, Andy, Togelius, Julian
This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers
Externí odkaz:
http://arxiv.org/abs/1702.00539
Akademický článek
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Levels are a key component of many different video games, and a large body of work has been produced on how to procedurally generate game levels. Recently, Machine Learning techniques have been applied to video game level generation towards the purpo
Externí odkaz:
http://arxiv.org/abs/1606.07487
Autor:
Liu, Jialin1 (AUTHOR), Snodgrass, Sam2 (AUTHOR), Khalifa, Ahmed3 (AUTHOR), Risi, Sebastian2,4 (AUTHOR), Yannakakis, Georgios N.2,5,6 (AUTHOR), Togelius, Julian2,3 (AUTHOR) julian.togelius@nyu.edu
Publikováno v:
Neural Computing & Applications. 2021, Vol. 33 Issue 1, p19-37. 19p.