More likely, playing it in order commits to a certain approach too early, and makes that approach predictable. Starting from the middle leaves other options open.

Given that it trains against a copy of itself, with equivalent predictive powers, it makes sense that it would pick moves that have lots of branching possibilities because that would increase its effectiveness against itself.

I've heard a similar approach described in military strategy at all levels: rather than looking for a single dominant tactic, you try with each "move" to create so many potentially-viable future positionings at once that your opponent cannot predict you in order to effectively concentrate their effort.

It'd be very scary to watch a "sibling" to AlphaGo play a 4X game.

DeepMind has set their sights on StarCraft II next: https://deepmind.com/blog/deepmind-and-blizzard-release-star...

Directly thought of StarCraft while reading this comment: never revealing your strategy and constantly attacking while being defensing and investing in economy to keep your opponent on the defensive. Like another commenter said, StarCraft 2 is DeepMind's next bet.

I am not too familiar with StarCraft but from what I can gather, it relies deeply on micromanagement. It seems to me that any half competent AI strategy wise would destroy any human player just with raw clicks per seconds during fights.

That's true, with StarCraft you have to basically limit the actions per minute the AI can perform to human levels, otherwise it can effortlessly out-control a human player. The game was only balanced to be fair for players with human capabilities, if you take that assumption away the unit and race balance goes totally off the rails.

There is the question of what your AI takes as its input though. If you feed it the rendered frames of the games there are a lot of open challenges in comparison to feeding it a friendlier representation of the game state.

There is also the question of teaching the AI a strategy vs only giving it the rules of the game and letting it develop strategies by itself.

In comparison to Chess or Go, StarCraft has a lot more game elements to understand and conceptualize. StarCraft is played with incomplete information, because of the fog of war - so for an AI to play it properly it has to have persistent knowledge of what it has seen of the game state and be able to draw conclusions from that about the probable overall state of the game.

As far as I'm aware they force the AI to only interact at a certain frequency, limiting this kind of advantage

You call it narrative when you understand what's going on.

You call it chaos when it's far above your head.

Good insight, I think there are implications for the Cynefin framework model that offers four possible state spaces: simple, complicated, complex, and chaotic. This classification may have as much to do with our state of information (or depth of understanding) as an objectively "chaotic state."

Made me think of this: [https://www.youtube.com/watch?v=fU7hZ3smj0g&t=4m22s](https:/...