Octavius,
a chess engine by Luke Pellen relying purely on an evaluation based on a four-layer neural network without any material analysis, choosing the move with the best score on its single output neuron, performing a one plylook ahead. Despite Sebastian Thrun's remark, that using the raw board representation as input of a neural network is a poor choice [1], but quite similar to one input representation of the Neural MoveMap Heuristic by Levente Kocsis et al. [2], Octavius has an input layer of 768 (12x64) nodes, a sequence of 12 numbers (11 or 12 times 0.0, 1 or 0 times 1.0) for each square, where a 1.0 was given for the particular piece (if any) residing on that square. Two hidden layers with 1024 and 512 nodes are feed forward to one output node, which represents the score of the position for one side to move. In total, Octavius' net has 2305 nodes with 1,311,232 connections.
a chess engine by Luke Pellen relying purely on an evaluation based on a four-layer neural network without any material analysis, choosing the move with the best score on its single output neuron, performing a one ply look ahead. Despite Sebastian Thrun's remark, that using the raw board representation as input of a neural network is a poor choice [1], but quite similar to one input representation of the Neural MoveMap Heuristic by Levente Kocsis et al. [2], Octavius has an input layer of 768 (12x64) nodes, a sequence of 12 numbers (11 or 12 times 0.0, 1 or 0 times 1.0) for each square, where a 1.0 was given for the particular piece (if any) residing on that square. Two hidden layers with 1024 and 512 nodes are feed forward to one output node, which represents the score of the position for one side to move. In total, Octavius' net has 2305 nodes with 1,311,232 connections.
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Selected Games
Beginning in 1999, Octavius performed 284,552 backpropagations up to April 26, 2004, when it won its first game against his creator [4]:See also
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