<p>I guess what you want is a ML system which plays chess?</p>

<p>One way to get it, is to see it as a pattern recognition problem. If you modeled it with a neural network, you could approach it like this:</p>

<ul>
<li>the input is a binary vector of size 768=64*2*6  (64=8*8 positions on the board, 2 Players, 6 different chess figures)</li>
<li>64 output neurons (one for each field).</li>
<li>The network would give you a clue which piece to move. It tries to <strong>predict which piece a human player would choose</strong>. If the network chooses something invalid, you can just go to the next most likely piece (assuming you make use of softmax at the end).</li>
</ul>

<p>You could train a second network which takes the same input. You might think about building a CNN with 12 feature maps instead of a MLP. This network should <strong>predict which player will win</strong>. Then you know which figure would be moved by a human with the first network and then you can find the "best" move with the second network.</p>

<h2>More resources</h2>

<p>You might be interested in <a href="http://arxiv.org/abs/1511.06410" rel="nofollow">Better Computer Go Player with Neural Network and Long-term Prediction</a> by Facebook Research.</p>
How to train a chess system with games played by humans (Machine Learning in Chess)
<p>As a part of my project, I'm now working on training a chess system with games played by humans. I have significant knowledge on machine learning but am clueless on how to proceed with this. Is this project too complex? Kindly advise on how to proceed. </p>