[batmansmk]

I guess there are reasons why researchers build chess programs: it is easy to compare performance between algorithms. When you can solve chess, you can solve a whole class of decision-making problems. Consider it as the perfect lab.

[EliRivers]

What is that class of decision-making problems? It's nice to have a machine really good at playing chess, but it's not something I'd pay for. What decision-making problems are there, in the same class, that I'd pay for?

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.

[jcelerier]

> What decision-making problems are there, in the same class, that I'd pay for?

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

[AstralStorm]

Perfect information problem solving is not interesting anymore.

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.

[dgacmu]

Strongly disagree. There are a lot of approximation algorithms and heuristics in wide use - to the tune of trillions of dollars, in fact, when you consider transportation and logistics, things like asic place & route, etc. These are all intractable perfect info problems that are so widespread and commercially important that they amplify the effect of even modest improvements.

(You said problems, not games...)

[AstralStorm]

Indeed, there are a few problems where even with perfect information you will be hard pressed to solve them. But that is only a question of computational power or the issue when the algorithm does not allow efficient approximation (not in APX space or co-APX).

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.

[dgacmu]

Perhaps a unifying way of saying this is: it's a research problem to figure out how to get ML techniques to the point they outperform existing heuristics on "hard" problems. Doing so will result in engineering improvements to the specific systems that need approximate solutions to those problems.

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.)

[eitland]

> Perfect information problem solving is not interesting anymore.

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.

[nl]

There’s plenty of interesting work on poker bots.

[AstralStorm]

Poker bots actually deal with a (simple) game with imperfect information. It is not the best test because short memory is sufficient to win at it.

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...

[djoyal]

I think chess may actually be the worst lab. Decisions made in chess are done so with perfect knowledge of the current state and future possibilities. Most decisions are made without perfect knowledge.

[Tepix]

For chess, the future possibilities are so vast, you can't call them "perfect knowledge" with a straight face.

[throwawaymath]

This is not what the terminology "perfect knowledge" means. Perfect knowledge (more often called "perfect information") refers to games in which all parts of the game state are accessible to every other player. In theory, any player in the game has access to all information contained in every game state up to the present and can extrapolate possible forward states. Chess is a very good example of a game of perfect information, because the two players can readily observe the entire board and each other's moves.

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 :)

[djoyal]

A human might not be able to, but a computer can. Isn't the explicit reason research shifted to using Go the fact that you can't just number crunch your way through it?

[DonaldFisk]

AlphaGo Zero did precisely that. Most of its computations were done on a huge array of GPUs. The problem with Go is that look-ahead is more of a problem than in Chess, as Go has roughly between five and ten times as many possible moves at each point in the game. So Go was more of a challenge, and master-level play was only made possible by advances in computer hardware.

[Moru]

Chess was already easy for computers. That's why Arimaa came to be.

[gaius]

When you can solve chess, you can solve a whole class of decision-making problems

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.

[raverbashing]

> 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.

[_Tev]

Those traits seem to me like a thing most people desperately need ... Everyone being confident in their assessment of everything seems like one of major problems of today's population.

[majewsky]

I think batmansmk doesn't mean "when X is good at chess, X is automatically good at lots of other things", but "the traits that make you a good chess player (given enough training) also make you good at lots of other things (given enough training)".

[EliRivers]

I might suspect (but certainly cannot prove) that the traits that make a human good at playing chess are very different to the traits that make a machine good at playing chess, and as such I don't think we can assume that the machine skilled-chess-player will be good at lots of other things in an analagous way to the human skilled-chess-player.

[SiempreViernes]

And Gaius point stands before this argument as well, chess is seen as such a weak predictor that playing a game of chess or requesting an official ELO rating isn't used for hiring screening for instance.

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.

[monktastic1]

Second world chess champion Emanuel Lasker spent a couple years studying Go and by his own report was dejected by his progress. Maybe he would have eventually reached high levels, but I've always found this story fascinating.

[taneq]

True, but I'd phrase it the other way around. The traits that make you (a human) good at general problem solving are also the traits that make you a good chess player. I do suspect, though, that there are some Chess-specific traits which boost your Chess performance but don't help much with general intelligence. (Consider, for example, the fact that Bobby Fischer wasn't considered a genius outside of his chosen field.)