The alphazero paper mentions that go has especially simple rules and that chess and shogi are more complicated. But the rules are still pretty clear. To some extend, they must have at least hard coded in the training data who won a particular game; or the game rules have to be hard coded. Something has to be hard coded. That is far from "nothing" prior!
But the paper is cryptic to me, overall ("Mastering Chess and Shogi by Self-Play with a
General Reinforcement Learning Algorithm").
Also, the parent comment is paradox. Data is information and that is something, so learning from data is learning from something.
If I remember correctly, in the "Mastering Chess etc" paper the structure of the neural net was mapped onto the chess board itself. The movement range of different pieces was also hard-coded into the network architecture -for example, there was a vector encoding "queen moves" and another for "knight moves" which taken together cover all possible chess piece moves. The encoding of the moves was not used to generate moves, per se- I don't remember the details very well but that was a separate module that I believe fed moves to the MCTS algorithm. The move range was encoded as a kind of constraint, to keep the network from exploring unproductive moves.
So the network didn't have to learn anything about the rules of chess, shogi or go and indeed, it did not. That knowledge was given to it directly. As far as I know, this is the done thing with most game-playing systems, especially ones for classic board games that have usually very simple rules that are easy to hand-code (so that there's no real need to learn them from scratch).
It would be interesting to see if it's possible to machine-learn the _rules_ of a game (i.e. given a move, recognise it as legal or not). A quick scan of internet search results confirms my recollection that most published work in game-playing agents focuses on learning how to play well, rather than how to play in the first place (indeed "learning to play chess" is used to mean "learning to win" in many publications).
But the paper is cryptic to me, overall ("Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm").
Also, the parent comment is paradox. Data is information and that is something, so learning from data is learning from something.