for my A-level computing project I was going to do an investigation into a type of engine design which incorporates Neural Networks that I haven't seen elsewhere; as part of this project I need to base my specification around the recipients of the outcomes for investigative projects (hopefully you guys).
My current design idea is this:
For a given position, A neural network will evaluate the position and spit out two values: how good it is for white/black, and a value representing how much will be gained from analysing this position further.
All of this information (along with the depth) could then be combined to determine whether the engine should analyse to a layer further down in the decision tree or return the first value (how good the position is for white) so that it could then be used as a "leaf" in the decision tree that could then be analysed by mini-max (along with suitable pruning algorithms) to give an overall analysis of a given move.
To train the neural network: I was going to play two networks against each other and analyse their evaluations of the certain moments in the game post-game to tell them (using back propagation) where they should look deeper or evaluate a certain position as better/worse based on who won the match. I think this method is similar to policy gradients. Another approach I was looking into was a genetic algorithm however I was sceptical of whether I have nearly enough processing power available to me seen as I'm doing this all on my laptop :(
I would be very grateful if you could answer these questions for me:
Have you seen any similar projects like this before?
What are any pitfalls that you could see with this method?
would you consider using a similar method in your engines? Why/ why not?
how would you alter this investigative project so that it could benefit you more personally?
Would you use the genetic algorithm approach?
Thanks a lot in advance for any replies!