An evolutionary tic-tac-toe player

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DC Field	Value	Language
dc.contributor.author	Al-Khateeb, Belal	-
dc.date.accessioned	2022-11-07T18:50:57Z	-
dc.date.available	2022-11-07T18:50:57Z	-
dc.date.issued	2012-11-04	-
dc.identifier.uri	http://localhost:8080/xmlui/handle/123456789/8205	-
dc.description.abstract	In this paper, artificial neural networks are used as function evaluators in order to evolve game playing strategies for the game of tic-tac-toe. The best evolved player is tested against an online perfect tic-tac-toe player, and also against a nearly perfect player which allows 10% random moves and finally against five selected human players. Those players are with different playing abilities. The results are promising, suggesting many other research directions.	en_US
dc.language.iso	en	en_US
dc.publisher	Int. J. Reasoning-based Intelligent Systems	en_US
dc.subject	artificial neural networks	en_US
dc.subject	tic-tac-toe	en_US
dc.subject	evolutionary algorithms	en_US
dc.title	An evolutionary tic-tac-toe player	en_US
dc.type	Article	en_US
Appears in Collections:	قسم علوم الحاسبات

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