Výsledky vyhledávání

The evaluation of procedural content generation (PCG) systems for generating video game levels is a complex and contested topic. Ideally, the field would have access to robust, generalisable and widely accepted evaluation approaches that can be used

Externí odkaz: http://arxiv.org/abs/2404.18657

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Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video Games

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

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The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation

Autor: Tokarchuk, Evgeniia, Niculae, Vlad

Continuous-output neural machine translation (CoNMT) replaces the discrete next-word prediction problem with an embedding prediction. The semantic structure of the target embedding space (i.e., closeness of related words) is intuitively believed to b

Externí odkaz: http://arxiv.org/abs/2310.20620

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Exploring Minecraft Settlement Generators with Generative Shift Analysis

Autor: Hervé, Jean-Baptiste, Withington, Oliver, Hervé, Marion, Tokarchuk, Laurissa, Salge, Christoph

With growing interest in Procedural Content Generation (PCG) it becomes increasingly important to develop methods and tools for evaluating and comparing alternative systems. There is a particular lack regarding the evaluation of generative pipelines,

Externí odkaz: http://arxiv.org/abs/2309.05371

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