Abstract
As virtual worlds in games become larger and more
detailed, the need for rich, interactive content to realistically
populate these worlds becomes greater. Content is expensive
and slow to create manually and does not scale once created.
Procedural content generation offers an attractive alternative for
providing this content. There are several methods for generating
different types of content including terrain and some types of
organisms. However, these methods are hard to control and
can give inconsistent results. A method of providing a high
level, semantic context to such procedural methods may offer a
potential solution. Such an approach may also allow manually
created content to be placed appropriately and in a scalable
manner in the world. Finally, semantic knowledge may be used
to annotate content to allow greater interactivity and improved
AI. This paper suggests such a method. Making use of semantic
networks for storing knowledge about the potential content of
the virtual world and using stateful graph traversal algorithms
to convert the semantic knowledge into concrete instances, this
method supports the procedural generation of rich complex
content for virtual worlds.
detailed, the need for rich, interactive content to realistically
populate these worlds becomes greater. Content is expensive
and slow to create manually and does not scale once created.
Procedural content generation offers an attractive alternative for
providing this content. There are several methods for generating
different types of content including terrain and some types of
organisms. However, these methods are hard to control and
can give inconsistent results. A method of providing a high
level, semantic context to such procedural methods may offer a
potential solution. Such an approach may also allow manually
created content to be placed appropriately and in a scalable
manner in the world. Finally, semantic knowledge may be used
to annotate content to allow greater interactivity and improved
AI. This paper suggests such a method. Making use of semantic
networks for storing knowledge about the potential content of
the virtual world and using stateful graph traversal algorithms
to convert the semantic knowledge into concrete instances, this
method supports the procedural generation of rich complex
content for virtual worlds.
| Original language | English |
|---|---|
| Title of host publication | 2014 6th International Conference on Games and Virtual Worlds for Serious Applications |
| Publication status | Published - 2014 |