Ideal Input In Neural Network For The Game Checkers

Question

I'm designing a feed forward neural network learning how to play the game checkers.

For the input, the board has to be given and the output should give the probability of winning versus losing. But what is the ideal transformation of the checkers board to a row of numbers for input? There are 32 possible squares and 5 different possibilities (king or piece of white or black player and free position) on each square. If I provide an input unit for each possible value for each square, it will be 32 * 5. Another option is that:

  Free Position: 0 0

  Piece of white: 0 0.5 && King Piece of white: 0 1

  Piece of black: 0.5 1 && King Piece of black: 1 0

In this case, the input length will be just 64, but I'm not sure which one will give a better result. Could anyone give any insight on this?

Answer 1

I've tried all possibilities and intuitive i can say that the most great idea is separating all possibilities for all squares. Thus, concrete:

0 0 0: free
1 0 0: white piece
0 0 1: black piece
1 1 0: white king
0 1 1: black king

It is also possible to enhance other parameters about the situation of the game like the amount of pieces under threat or amount of possibilities to jump.