4 ms·

Can somebody explain why poker is a big deal? I don't play poker and I don't see why is it complex for an AI. I can see why chess/go could be a big deal: there'

by krick 10y ago

Can somebody explain why poker is a big deal? I don't play poker and I don't see why is it complex for an AI. I can see why chess/go could be a big deal: there's definitely a solution to the game, but the space is too large for it to be computed, so you have to optimize somehow, which real players somewhat can. When computers were slow, humans did it better than computers. Poker, I thought, doesn't really have any solutions, it is largely about luck, no one really knows what's going on, so aside from some probabilistic computations it is about guessing what your opponent thinks while not giving up what you think. Choosing and seeing the behavior. I would think that playing against AI is generally pointless, since computers don't worry. I mean, surely there are some general guidelines on strategy, since somebody plays better that the others, but following it shouldn't be harder for a computer than for a human, quite the opposite. So, what is true?

Cybiote 10y ago
Poker in a sense, is the most realistic game in that it has multiplayer, hidden information and stochastic aspects. While chess and go have huge state spaces (as does no limit poker), they are also fairly straight forward due to their 2 player perfect information nature. If say you took an algorithm key to alphago, something like UCT, and tried to apply it to poker, it would diverge from nash equilibrium. Nash Equilibrium by the way, is the sense in which the game has solutions. In poker, you're looking at your expected value per decision (good players also do this at a meta-level), you're trying to place a bound on the other players' holdings and trying to bluff at an optimal rate. A good poker bot has to be capable of all this, which has proved extremely difficult. This is why it has proven difficult until just about this year, for researchers to be able to clearly say that at least in the heads up case and given enough compute to train, bots are at expert human level. What might turn out interesting is the fact that though headsup is more difficult for humans and multiplayer is arguably the simpler, the reverse seems to be true for bots. The state space multiplies such that naive application of equilibrium play and tree search simply do not cut it.
- krick 10y ago
  I don't understand. Maybe there are some good resources to read, so I could see it better? Essentially, you are explaining why poker it hard. This is no surprise, and I already included it into my question to an extent. The real question is, why is it harder for computers than for humans? Both seem to be completely oblivious to what is truly going on due to imperfect information nature of poker, and both seem to be able to make simple estimations. Computers are better at multiplying fractions (needed to estimate probability) and computers are better at not-worrying (needed to bluff well). I'd assume it is humans who are at disadvantage, and your post doesn't explain (it seems) why is it the otherwise.
  - spectrum1234 10y ago
    As said before because of hidden information the optimal strategy appears to diverge unless you have more (all) the information in the decision tree. Basically you can never make a decision with the specific hand you have in isolation without knowing what you'd do with every other hand (and same for your opponent). This is very different than chess/go! So while poker may even have a simpler game tree than chess/go you need to have more of it upfront to solve the game, and being "off" means much much greater error. Depth matters less than breadth. Said another way in chess/go you can brute force get near optimal for the current decision, then reset and do again for the next decision. But with poker you have to solve for ALL POSSIBLE decisions (hands) up front (and inclusive of all possible board situations), and THEN do AGAIN 3 more times (pre-flop flop turn river) plus the numerous times you are raised means this is much more than 4 times total. Example: Bot is out of position with AdAh on AsKsQc facing a big raise. In isolation its probably best to re-raise and simulations show this makes the most money. But you also need to account for the times you have AT (no flush draw) and need to save AT money. But this logic doesn't stop here. Typically on the river out of position you will check the majority of your range if your opponent has bet every street (this is one of the few "proven" strategies because you always want the pot smaller out of position on average). So, you literally need to have the entire game solved in order to know HOW TO GET TO THE RIVER in a balanced way with good and bad hands. And, getting to the river to play profitably likely means giving up profitability on every other street - this is the diverging from optimal that default algorithms will be wrong on unless they have the entire game tree in front of them.
spectrum1234 10y ago
Its only about luck in the short term.

by krick 10y ago

Cybiote 10y ago

Poker in a sense, is the most realistic game in that it has multiplayer, hidden information and stochastic aspects. While chess and go have huge state spaces (as does no limit poker), they are also fairly straight forward due to their 2 player perfect information nature. If say you took an algorithm key to alphago, something like UCT, and tried to apply it to poker, it would diverge from nash equilibrium. Nash Equilibrium by the way, is the sense in which the game has solutions. In poker, you're looking at your expected value per decision (good players also do this at a meta-level), you're trying to place a bound on the other players' holdings and trying to bluff at an optimal rate. A good poker bot has to be capable of all this, which has proved extremely difficult. This is why it has proven difficult until just about this year, for researchers to be able to clearly say that at least in the heads up case and given enough compute to train, bots are at expert human level. What might turn out interesting is the fact that though headsup is more difficult for humans and multiplayer is arguably the simpler, the reverse seems to be true for bots. The state space multiplies such that naive application of equilibrium play and tree search simply do not cut it.

krick 10y ago
I don't understand. Maybe there are some good resources to read, so I could see it better? Essentially, you are explaining why poker it hard. This is no surprise, and I already included it into my question to an extent. The real question is, why is it harder for computers than for humans? Both seem to be completely oblivious to what is truly going on due to imperfect information nature of poker, and both seem to be able to make simple estimations. Computers are better at multiplying fractions (needed to estimate probability) and computers are better at not-worrying (needed to bluff well). I'd assume it is humans who are at disadvantage, and your post doesn't explain (it seems) why is it the otherwise.
- spectrum1234 10y ago
  As said before because of hidden information the optimal strategy appears to diverge unless you have more (all) the information in the decision tree. Basically you can never make a decision with the specific hand you have in isolation without knowing what you'd do with every other hand (and same for your opponent). This is very different than chess/go! So while poker may even have a simpler game tree than chess/go you need to have more of it upfront to solve the game, and being "off" means much much greater error. Depth matters less than breadth. Said another way in chess/go you can brute force get near optimal for the current decision, then reset and do again for the next decision. But with poker you have to solve for ALL POSSIBLE decisions (hands) up front (and inclusive of all possible board situations), and THEN do AGAIN 3 more times (pre-flop flop turn river) plus the numerous times you are raised means this is much more than 4 times total. Example: Bot is out of position with AdAh on AsKsQc facing a big raise. In isolation its probably best to re-raise and simulations show this makes the most money. But you also need to account for the times you have AT (no flush draw) and need to save AT money. But this logic doesn't stop here. Typically on the river out of position you will check the majority of your range if your opponent has bet every street (this is one of the few "proven" strategies because you always want the pot smaller out of position on average). So, you literally need to have the entire game solved in order to know HOW TO GET TO THE RIVER in a balanced way with good and bad hands. And, getting to the river to play profitably likely means giving up profitability on every other street - this is the diverging from optimal that default algorithms will be wrong on unless they have the entire game tree in front of them.

spectrum1234 10y ago

Its only about luck in the short term.