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Procedural Content Generation Techniques and Approaches

Authors: Mawed Mohamad Abdullah;

Procedural Content Generation Techniques and Approaches

Abstract

Nowadays, using PCG gains more and more importance, and PCG plays an increasingly important role in various areas. Particularly in game design and development where PCG have made a qualitative leap forward, especially in recent years. In fact, PCG is a very broad discipline. Investigating of this matter is so interesting, and satisfaction is guaranteed. This thesis mainly reviews the primary techniques, approaches and algorithms that are usually used in PCG. Most of the studied approaches and techniques are considered state-of-the-art. This thesis goes beyond the classical way of demonstrating how PCG techniques works unlike any traditional research about this subject. The primary focus is on the practical side rather than purely theoretical matters. To experience how PCG works in action, over 20 different techniques will be demonstrated, implemented in code line by line and tested. After running each algorithm, the results will be discussed and a comparison with other techniques will be done if that makes sense. At the end of this thesis, the strengths and the weaknesses of each technique will be highlighted. At the end of this thesis, an infinite terrain will be implemented as a practical part of PCG.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green