Consider it as the perfect lab.

Seems like a lab so simplified that I'm unconvinced of its general applicability. Perfect knowledge of the situation and a very limited set of valid moves at any one time.

an awful lot of graph and optimization problems. See for instance some examples in https://en.wikipedia.org/wiki/A*_search_algorithm

Did they manage to extend it to games with hidden and imperfect information?

(Say, chess with fog of war also known as Dark Chess. Phantom Go. Pathfinding equivalent would be an incremental search.)

Edit: I see they are working on it, predictive state memory paper (MERLIN) is promising but not there yet.

(You said problems, not games...)

The thing is, an algorithm that can work with fewer samples and robustly tolerating mistakes in datasets (also known as imperfect information) will be vastly cheaper and easier to operate. Less tedious sample data collection and labelling.

Working with lacking and erroneous information (without known error value) is necessarily a crucial step towards AGI; as is extracting structure from such data.

This is the difference between an engineering problem and research problem.

I completely agree about the importance of imperfect information problems. In practice, many techniques handle some label noise, but not optimally. Even MNIST is much easier to solve if you remove the one incorrectly-labeled training example. (one! Which is barely noise. Though as a reassuring example from the classification domain, JFT is noisy and still results in better real world performance than just training on imagenet.)

I guess in the same way as lab chemistry isn't interesting anymore ? (Since it often happens in unrealistically clean equipment :-)

I think there is nothing preventing lab research from going on at the same time as industrialization of yesterday's results. Quite on the contrary: in the long run they often depend on each other.

The real challenge is to devise a general algorithm that will learn to be a good poker player in thousands of games, strategically, from just a bunch of games played. DeepStack AI required 10 million simulated games. Good human players outperform it at intermediate training stages.

And then the other part is figuring out actual rules of a harder game...

A good example of a game of imperfect information is poker, because players have a private hand which is known only to them. Whereas all possible future states of a chess game can be narrowed down according to the current game state, the fundamental uncertainty of poker means there is a combinatorial explosion involved in predicting future states. There's also the element of chance in poker, which further muddies the waters.

Board games are often (but not always) games of perfect and complete information. Card games are typically games of imperfect and complete information. This latter term, "complete information", means that even if not all of the game state is public, the intrinsic rules and structure of the game are public. Both chess and poker are complete, because we know the rules, win conditions and incentives for all players.

This is all to say that games of perfect information are relatively easy for a computer to win, while games of imperfect information are harder. And of course, games of incomplete information can be much more difficult :)

If this were true, there would be a vast demand for grandmasters in commerce, government, the military... and there just isn’t. Poker players suffer from similar delusions about how their game can be generalised to other domains.

Oh that's so true

Poker players in the real life would give up more often than not, whenever they didn't know enough about a situation or they didn't have enough resources for a win with a high probability.

And people can call your bluff even if you fold.

I suspect that chess as a metagame is just so far developed that being "good at chess" means your general ability is really overtrained for chess.