A New Machine-Learning Technique Applied to the Game of Checkers

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5896

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DC Field	Value	Language
dc.creator	Griffith, Arnold	-
dc.date	2004-10-04T14:08:38Z	-
dc.date	2004-10-04T14:08:38Z	-
dc.date	1966-03-01	-
dc.date.accessioned	2013-10-09T02:41:50Z	-
dc.date.available	2013-10-09T02:41:50Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-094	-
dc.identifier	http://hdl.handle.net/1721.1/5896	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	This paper described a recent refinement of the machine--learning process employed by Samuel (1) in connection with his development of a checker playing program. Samuels checker player operates in much the same way a human player does; by looking ahead, and by making a qualitative judgment of the strength of the board positions it encounters. A machine learning process is applied to the development of an accurate procedure for making this strength evaluation of board positions. Before discussing my modifications to Samuels learning process, I should like to describe briefly Samuel's strength evaluation procedure, and the associated learning process.	-
dc.format	7 p.	-
dc.format	5529659 bytes	-
dc.format	362611 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-094	-
dc.title	A New Machine-Learning Technique Applied to the Game of Checkers	-
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