Semantic Content Generation Framework for Game Worlds

Research output: Chapter in Book/Report/Published conference proceedingConference contribution

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.
Original languageEnglish
Title of host publication2014 6th International Conference on Games and Virtual Worlds for Serious Applications
Publication statusPublished - 2014

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Bennett, M. (2014). Semantic Content Generation Framework for Game Worlds. In 2014 6th International Conference on Games and Virtual Worlds for Serious Applications
Bennett, Mark. / Semantic Content Generation Framework for Game Worlds. 2014 6th International Conference on Games and Virtual Worlds for Serious Applications. 2014.
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abstract = "As virtual worlds in games become larger and moredetailed, the need for rich, interactive content to realisticallypopulate these worlds becomes greater. Content is expensiveand slow to create manually and does not scale once created.Procedural content generation offers an attractive alternative forproviding this content. There are several methods for generatingdifferent types of content including terrain and some types oforganisms. However, these methods are hard to control andcan give inconsistent results. A method of providing a highlevel, semantic context to such procedural methods may offer apotential solution. Such an approach may also allow manuallycreated content to be placed appropriately and in a scalablemanner in the world. Finally, semantic knowledge may be usedto annotate content to allow greater interactivity and improvedAI. This paper suggests such a method. Making use of semanticnetworks for storing knowledge about the potential content ofthe virtual world and using stateful graph traversal algorithmsto convert the semantic knowledge into concrete instances, thismethod supports the procedural generation of rich complexcontent for virtual worlds.",
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Bennett, M 2014, Semantic Content Generation Framework for Game Worlds. in 2014 6th International Conference on Games and Virtual Worlds for Serious Applications.

Semantic Content Generation Framework for Game Worlds. / Bennett, Mark.

2014 6th International Conference on Games and Virtual Worlds for Serious Applications. 2014.

Research output: Chapter in Book/Report/Published conference proceedingConference contribution

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PY - 2014

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N2 - As virtual worlds in games become larger and moredetailed, the need for rich, interactive content to realisticallypopulate these worlds becomes greater. Content is expensiveand slow to create manually and does not scale once created.Procedural content generation offers an attractive alternative forproviding this content. There are several methods for generatingdifferent types of content including terrain and some types oforganisms. However, these methods are hard to control andcan give inconsistent results. A method of providing a highlevel, semantic context to such procedural methods may offer apotential solution. Such an approach may also allow manuallycreated content to be placed appropriately and in a scalablemanner in the world. Finally, semantic knowledge may be usedto annotate content to allow greater interactivity and improvedAI. This paper suggests such a method. Making use of semanticnetworks for storing knowledge about the potential content ofthe virtual world and using stateful graph traversal algorithmsto convert the semantic knowledge into concrete instances, thismethod supports the procedural generation of rich complexcontent for virtual worlds.

AB - As virtual worlds in games become larger and moredetailed, the need for rich, interactive content to realisticallypopulate these worlds becomes greater. Content is expensiveand slow to create manually and does not scale once created.Procedural content generation offers an attractive alternative forproviding this content. There are several methods for generatingdifferent types of content including terrain and some types oforganisms. However, these methods are hard to control andcan give inconsistent results. A method of providing a highlevel, semantic context to such procedural methods may offer apotential solution. Such an approach may also allow manuallycreated content to be placed appropriately and in a scalablemanner in the world. Finally, semantic knowledge may be usedto annotate content to allow greater interactivity and improvedAI. This paper suggests such a method. Making use of semanticnetworks for storing knowledge about the potential content ofthe virtual world and using stateful graph traversal algorithmsto convert the semantic knowledge into concrete instances, thismethod supports the procedural generation of rich complexcontent for virtual worlds.

M3 - Conference contribution

BT - 2014 6th International Conference on Games and Virtual Worlds for Serious Applications

ER -

Bennett M. Semantic Content Generation Framework for Game Worlds. In 2014 6th International Conference on Games and Virtual Worlds for Serious Applications. 2014