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by spywaregorilla
1436 days ago
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This is all technically true, but it's also misleading I feel. Chess and Go have a lot of states, and you need all of them. The entropy of the games are enormous. Move that bishop and the significance of every piece on the board could change. Stratego has verrrryy low entropy. You can only move one piece one space at a time aside from scouts. You can also enter scenarios where the game just simplifies on some dimensions immensely to which some fairly dumb algorithms can win the game. If the enemy loses their top dude and their spy, your top guy is invincible against anything that has ever moved, which isn't victory assured but its probably relatively easy to write an ai to win from that point on. Maybe this is just me but when I thought about stratego I'm pretty sure I only thought about a few units at a time. Moving all of them is a bad idea because of bombs anyway. Your mental model of the game does not consider the state of units in the back row of either side. It probably doesn't even consider all of the units in the front. I also suspect a lot of optimal play is just doing dumb shit back and forth to force your opponent into making the more aggressive move in a lot of cases without technically forfeiting which most humans likely wouldn't do but ai would like to do if not constrained otherwise. But I agree a simple tree search on possible moves is unlikely to work very well until the end game. |
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Of course, we ought to be focused on the situations which lead to these nice situations - which leads us back to the imperfect information situations which lead to those positions we can reason about.
Interestingly, you can invert your point and get a similar rule more generally. The trick is to backward induction on the abstracted category transitions. So your point is actually so valid that is valid even in situations where your examples don't apply. I hope you can see I'm strong manning here. I agree with you that this should be done and is critical to making things tractable.
There is an issue though, at least in the generalized case, which is that perfect information is much easier than imperfect information.
See, in perfect information games when you do the backward induction step you actually just flat out solve the game. Meanwhile, in imperfect information games, this backward induction step merely makes solving the game more tractable. Chess endgames are the backward induction version of your forward induction from the rules abstraction, but the abstraction rule is so hard to determine we wouldn't usually think of it that way. Notice that chess is hard enough that we don't have endgame tables that go all the way back to the start of the game? Yet the average game length in Stratego is hundreds of moves longer and there are more then double the number of pieces.
I still think your point is right and someone who pushes hard enough in this direction would manage to tackle the complexity. So I basically agree with you. But if you take standard approaches like counterfactual regret minimization and throw it at the game without adjusting them for the fact that the problem is "hard" then they just wont terminate.