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Comment by d3ckard | Hacker News Reader

While I wouldn't exclude possible breakthrough, it's definitely a different case. Go was feared because of an enormous number of possible states, but AlphaGo circumvented the issue by not caring about it. It didn't "solve" Go, it just plays it better than humans.

Starcraft 2 state is much bigger than Go's and requires real time action. Also, actions have to be performed in parallel in real time, which probably requires different techniques and probably puts a way lower bound on number of iterations doable in given time.

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I always believed that one of the core issues with Starcraft is considered to be that it is not a perfect information game (in addition to inherent complexity). Having to deal with learning how the opponent plays, and basically guess and make inferences about what they are doing, is somewhat new territory.

Real time and parallelism makes the computers job easier not harder.

Yeah, not really. Ask anybody working on distributed systems.

When AlphaZero plays Go with itself, it can basically go as fast as it can calculate next move. With Starcraft that's not the case - both networks have to work in sync, probably need some temporal awareness and probably will have some limit of actions per time fraction, which basically requires a whole new approach. Of course, I can be gravely mistaken, but I would like to now how they can circumvent this.

Could a similar strategy apply then?

> state is much bigger.

Can AlphaGo not care about it and just play better and faster?

In SC there are time constraints in that you're getting resources at a certain rate. You have to grow your capacity to pull resources and at the same time be building units to defend yourself. If you allocate resources poorly, you'll find yourself losing. AlphaGo can't ignore that, but...

I think AI does have an advantage once it starts to be competent, especially if it's interacting through APIs exclusively and not the interface, which means that it's actions per minute could be astronomically higher than a human player, with unheard of levels of micro. At the same time, I think machine learning is almost an idea solution to figuring out build orders. It's gonna be fast and smart. Question just is how long?

> especially if it's interacting through APIs exclusively and not the interface, which means that it's actions per minute could be astronomically higher than a human player, with unheard of levels of micro

There's no way they'll compete under unlimited APM rules, it wouldn't even be remotely interesting. We're trying to match wits with the AI, not the inertia and momentum of super slow fingers, keys, and mouse.

I'm sure they'll come up with an "effective APM" heuristic which compares similarly to top pros, and feed it as a constraint to the AI.

Given the success with Go and chess, I would guess less than a year.

For human vs AI there will have to be APM rate limits otherwise the AI can win with no real strategy[0].

For AI vs AI it could be interesting to see what strategies develop with two opponents with the ability to perfectly microcontrol their units.

[0]Zerglings vs Siege Tanks when controlled by AI with perfect micro.

https://www.youtube.com/watch?v=IKVFZ28ybQs

I think even with unlimited APM, humans can still beat AI using cheese strategies, like an undetected cannon rush, since you can't really micro your workers against cannons (the projectile isn't dodge-able like the one from siege tanks).

Otherwise, you make a fair point and that video is amazing. AI vs AI strategy with unlimited APM would be very exciting to watch.

It is generally assumed that SC and SC2 are not actually just Rock Paper Scissors. That is, you're not obliged to guess your opponent's strategy and counter in the dark but can instead "scout" and figure out what they're doing and overall this can beat a "blind" strategy like cannon rush that doesn't respond to what the opponent's strategy is.

For example the "All ravens, all the time" Terran player Ketrok just responded to the surge in popularity of Cannon Rushes by making a tiny tweak to his opening worker movement. The revised opening spots the Cannon Rush in time to adequately defend and thus of course win.

There's also the fact that an action you trigger now (build unit) doesn't have immediate payout (building takes time). In Chess it can evaluate the current board as is, and it gets immediate feedback on each move.

I'd be interested to see if it can "plan ahead". Maybe a Chess variant where you have to submit your next move before the current player moves, or something like that.

It is not a sequential perfect information game. Information is imperfect, actions can be taken by both sides at the same time, there probably will be action limit per time/game frame and the network will have to determine not only its next move, but also manage time for calculating that move. It totally changes the challenge.

As far as I understand (and I am no expert at all), AlphaGo basically creates a heuristic of what move to play in a given situation (which heurisitic is created by playing against itself many, many times). Instead of trying to "break" the game, they just decided to simulate playing and results were good enough to outmatch humans, but we have no idea how close to the "perfect game" AlphaGo actually got.

But - whole input to a network is 19x19 array with 3 possible states per cell, plus maybe turn count and one bit for determining whose next move is. S2 network should process graphic stream (lets say 1280/720), needs spatial awareness(minimap), priority setting and computational resource management. And it has to be fast enough in the first place just to follow the game.

I'm not saying that won't happen (who predicted Go breakthrough?), but it at least seem like a much bigger challenge.

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