[Lawtonfogle]

If there is magic to be found, it may be in that question. Why about graphs (namely the subset that are deep neural networks) allow them to not only contain such powerful heuristics, but also allow them to be created from scratch with barely any knowledge of the problem domain.

As a side note, I was playing a board game last night (Terra Mystica I believe) and wondering if you could get 5 different neural networks to play the game and then train them against each other (and once they are good enough, against players). I wonder how quickly one could train a network that is unbeatable by humans? Maybe even scale it up to training it to play multiple board games til it is really good at all of them before setting it lose on a brand new one (with a similar genre). Maybe Google could use this to make a Go bot.

But what happens if this is used for evil instead? Say a neural network that reads a person's body language and determines how easily they can be intimidated by either a criminal or the government. Or one that is used to hunt down political dissidents. Imagine the first warrant to be signed by a judge for no reason other than a neural network saying the target is probably committing a crime...

[thaumasiotes]

The best Go bot approach (as of some years ago, but it's not like neural networks are a new idea) uses a very different strategy. Specifically, the strategy of "identify a few possible moves, simulate the game for several steps after each move using a very stupid move-making heuristic instead of using this actual strategy recursively, and then pick the move that yielded the best simulated board state".

[deepnet]

Monte Carlo Tree Search ( Random playout ) is currently the best computer strategy for evaluating a Go position.

This is likely due to the way Go works , random playout provides a rough estimate of who controls what territory ( this is how Go is scored ).

Recently two deep-learning papers showed very impressive results.

http://arxiv.org/abs/1412.3409

http://arxiv.org/abs/1412.6564

The neural networks were tasked with predicting what move an expert would make given a position.

The MCTS takes a long time 100,000 playouts are typical - once trained the neural nets are orders of magnitude faster.

The neural nets output a probability for each move ( that an expert would make that move ) - all positions are evauluated in a single forward pass.

Current work centers around combining the two approaches, MCTS evaluates the best suggestions from the neural net.

Expert Human players are still unbeatable by computer Go.

[deepnet]

For Chess see David Silver's work on TreeStrap

It learns to master level from self-play.

http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Applications_fil...

also his lecture bootstrapping from tree based search

http://www.cse.unsw.edu.au/~cs9414/15s1/lect/1page/TreeStrap...

and Silver's overview on board game learning

http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching_files/g...

[arielby]

The "use a stupid heuristic as part of the evaluation function" is is, in fact, also an important part of Chess AI's mode (as Quiescence Search), through for different reasons.

[zwegner]

> Maybe Google could use this to make a Go bot.

There was in fact a group within Google that worked on this: http://www.cs.toronto.edu/~cmaddis/pubs/deepgo.pdf

[deepnet]

and the follow up from Google's Deepmind group :

Move Evaluation in Go Using Deep Convolutional Neural Networks Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver

http://arxiv.org/abs/1412.6564

[z92]

Before clicking I was assuming it would fail. Then read this in the summary: "When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates a million positions per move."