Artificial Neural Network Based Adaptive Chess Playing Machine
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Abstract
Various research projects have attempted to build a program that learns to play chess game, given little or no prior knowledge beyond the
rule of the game. A typical chess playing engine exhaustively explores the moving possibilities from a chessboard configuration to choose what the
next best move to play is. The brute- force method used by the Deep Blue chess machine has made huge impact in the field of artificial intelligence,
but is immensely resource hungry. This paper presents a very simple and efficient approach to develop an intelligent chess engine which will hint at
the best possible move using the evolutionary and adaptive computing technique on learning from the human grandmasters.
Keywords: Artificial Neural Network; Championship game; En passant; legal move; Octavius
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