I agree that it is surprising that Reinforcement Learning actually works
for a "strategy" game. I wonder if the fact that the layout of the table
can change so dramatically from one move to the other makes Reinforcement
Learning a good approach for Othello. That is, I wonder if it works for other
games like chess where the layour of the table does not change so much.


On other things, I would like to comment on an idea that was presented
during the presentation and I have always found intriguing: For games like
chess and Othello, where it is computationally impossible to calculate all
moves, we usually compare any strategy developed against a human
player in order to make the results credible. But what happens when the game
actually IS tractable? Should the evaluation of our strategy change? Put in
another way, consider how the results presented change for a 6x6 Othello board,
where there exists a strategy where blacks are assured to win. The algorithm
presented by the authors (or any algorithm anyway) would NEVER win!
But in 8x8 Othello, there exists a strategy such that blacks will always win,
it's just that no one has been able to find it yet because it is untractable.
So I find it disturbing that this ethereal being called tractability should
haunt any heuristic developed, seeing that AI is full of heuristics. Or
maybe this paradox defines the field?

Hector