Evolutionary neural networks for non-player characters in Quake III

Joost Westra; Frank Dignum

doi:10.1109/CIG.2009.5286460

Evolutionary neural networks for non-player characters in Quake III

Joost Westra, Frank Dignum

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Designing and implementing the decisions of Non-Player Characters in first person shooter games becomes more difficult as the games get more complex. For every additional feature in a level potentially all decisions have to be revisited and another check made on this new feature. This leads to an explosion of the number of cases that have to be checked, which in its turn leads to situations where combinations of features are overlooked and Non-Player Characters act strange in those particular circumstances. In this paper we show how evolutionary neural networks can be used to avoid these problems and lead to good and robust behavior. ©2009 IEEE.

Original language	English
Title of host publication	CIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games
Pages	302-309
DOIs	https://doi.org/10.1109/CIG.2009.5286460
Publication status	Published - 2009
Externally published	Yes
Event	CIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games - , Italy Duration: 7 Sept 2009 → 10 Sept 2009

Conference

Conference	CIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games
Country/Territory	Italy
Period	7/09/09 → 10/09/09

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10.1109/CIG.2009.5286460

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title = "Evolutionary neural networks for non-player characters in Quake III",

abstract = "Designing and implementing the decisions of Non-Player Characters in first person shooter games becomes more difficult as the games get more complex. For every additional feature in a level potentially all decisions have to be revisited and another check made on this new feature. This leads to an explosion of the number of cases that have to be checked, which in its turn leads to situations where combinations of features are overlooked and Non-Player Characters act strange in those particular circumstances. In this paper we show how evolutionary neural networks can be used to avoid these problems and lead to good and robust behavior. {\textcopyright}2009 IEEE.",

author = "Joost Westra and Frank Dignum",

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note = "CIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games ; Conference date: 07-09-2009 Through 10-09-2009",

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AU - Dignum, Frank

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AB - Designing and implementing the decisions of Non-Player Characters in first person shooter games becomes more difficult as the games get more complex. For every additional feature in a level potentially all decisions have to be revisited and another check made on this new feature. This leads to an explosion of the number of cases that have to be checked, which in its turn leads to situations where combinations of features are overlooked and Non-Player Characters act strange in those particular circumstances. In this paper we show how evolutionary neural networks can be used to avoid these problems and lead to good and robust behavior. ©2009 IEEE.

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T2 - CIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games

Y2 - 7 September 2009 through 10 September 2009

ER -

Evolutionary neural networks for non-player characters in Quake III

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