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About Procedural Content Generation Via Machine Learning
This document summarizes a research publication that explores procedural content generation (PCG) via machine learning (PCGML). PCGML uses machine learning models trained on existing content to generate new game content, as an alternative to traditional PCG methods like search-based or solver-based approaches. The publication surveys various PCGML techniques including neural networks and deep learning methods, and discusses open challenges like learning from small datasets and multi-layered learning. It covers applications of PCGML for content generation, repair, critique and analysis using different data representations and sources.
Detailed Information
| Author: | ['Marina MEL'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 16 |
| Language: | English |
| Format: | |
| Price: | FREE |
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