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Commentary on Abstraction Methods for Game Theoretic Poker


by Nick Christenson

This article originally appeared in the June, 2009 issue of TwoPlusTwo Magazine.

Motivation

Mathematics and computer science resarchers are performing a great deal of research into determining better strategies for playing poker. In fact, I will make the prediction that going forward, a majority of new breakthroughs in poker strategy will be accomplished on the basis of a combination of advanced mathematics or computer simulation.

As a consequence, those poker players who wish to learn (or continue) to play at the highest levels will want to understand the research that is being performed by academics about the game of poker. Unfortunately, few people without a strong background in the mathematical sciences are going to be able to interpret this research. Moreover, it is common that the authors of these papers are not advanced poker players themselves, and one can be certain that few among their target audience fit this criterion.

Some poker players may find it useful to have the findings of these papers explained in plain language, especially with an eye to how they might be adapted to real-world poker situations. The goal of this article series is to do just this. These articles are intended to bridge the gap between the sophisticated poker player and poker-related research that results in a scientific publication.

Each of these articles is based on a specific publication. I expect that the reader of these articles will also want to read the publications on which they are based, but it is my hope that this will not prove to be strictly necessary, and that the reader will still find something worthwhile here even if they choose not to study the original research.

Despite the effort to reduce the number and complexity of advanced mathematical concepts, and despite every attempt to restate the most obscure jargon into less obscure language, I expect that these articles will still demand more effort on the part of the reader than the typical poker article. I hope the topics discussed here prove worth the effort.

Introduction

The first article I've chosen to discuss is motivated by a discussion on the twoplustwo.com Books and Publications forum. The paper in question is titled "Abstraction Methods for Game Theoretic Poker", and it was published in Computers and Games: Second International Conference, CG 2000, Hamamatsu, Japan in October, 2000. It is available in PostScript format online at: athos.rutgers.edu/~mlittman/attweb/papers/cg00-poker.ps. Not everyone can easily decode a document in PostScript format, but recent versions of Adobe Acrobat(TM) should be able to handle it.

Objectives

The paper begins by describing two approaches for developing a game strategy: (1) a game-theoretic approach, and (2) a learning approach. The authors attribute the learning approch to poker researcher Darse Billings (of the University of Alberta games research group, the folks responsible for Poki Poker, Polaris, and a great deal of the most important poker research performed.) I believe the implication that Billings is dismissive of game-theoretic approach to be unfair, but perhaps he would accept this mantle.

The second approach is game-theoretic, in which we mathematically determine a game's optimum strategy. This is the approach taken for most examples in Mathematics of Poker by Bill Chen and Jerrod Ankenman. There are certainly advantages to a game-theoretic approach, not the least of which is that if you come up with a game-theoretic solution, you can prove its correctness. You can't do this with a learned strategy, and being able to prove correctness is strong stuff. The authors also point out that finding an optimal strategy, even for "toy" poker games, "... can lead to unexpected insights into the structure of the game."

In Section 2, the authors provide some background about game-theoretic solutions to games with hidden information (such as poker) using some simple games as exmaples (including a game with only three cards, a K, a Q, and a J that's identical to the AKQ game that gets significant mention in Mathematics of Poker.) Then they provide an equation for counting the number of decision points in a game, thus providing a measure of the complexity for two player poker-like card games with various size decks, numbers of betting rounds, numbers of bets and raises allowed, and the numbers of cards dealt in each round. If one or more of these parameters grows beyond a certain size, it becomes intractable to solve highly complex mathematicsl problems directly.

So, the authors discuss two methods by which the complexity of these games can be reduced, and each of these gets its own section of the paper. These are the "Abstraction" methods mentioned in the title of the paper. The first of these methods the authors call "binning", the second they call "independence of betting rounds".

Primary Research

Let's pretend I'm playing 7-card stud, my starting cards are split queens with an off-suit 6. Assuming there's no difference in the liveness of my kicker, I'm going to play this hand exactly the same way I'd play split queens with an off-suit 7. In fact, I can come up with a range of hands that I'd play in exactly the same way. Whenever I have hands I would play in the exactly or very close to the same way, I can consider them to all be the same from a strategic point of view, and I can put them all in the same "bin".

The fewer the total number of bins, the easier it becomes to do the math, but the resulting simplified strategy becomes less accurate. The authors created a test game with an ante of 1 chip, a possible bet and call or fold of 1 chip, and 200 possible starting hands. They found an optimal strategy for the complete game version of this game, and then divided the starting hand space into different numbers of bins and compared the effect of this on the win rate. Twenty bins works pretty well in this game, and once you get into the 40-50 bin range, the results look pretty similar to the results for the un-abstracted game.

They made the game more complex and a little more poker-like and ran it again, achieving similar results. Clearly, for complex games grouping similar starting hands can reduce the overall complexity of the calculations necessary to come up with a strong strategy, and if the grouping is done judiciously, the impact to the bottom line is likely to be minimal. Of course, this doesn't help us come up with good groupings for real poker games, but it's a proof of concept.

In this simplified games, there's no such thing as a hand with "potential". That is, there are no drawing hands. In order to explore this aspect, they modified the game to include drawing type hands and reran their binning experiment, obtaining similar results.

In Section 4 the authors introduce a new game that's a little more complex than their previous two. They call this game "Rhode Island hold'em", as a smaller, but structurally similar, game to "Texas hold'em". In case it isn't immediately obvious, this is the sort of joke that mathematics PhDs find amusing. As an aside, there exists a paper about finding an optimum solution for this game: Optimal Rhode Island hold'em Poker by Andrew Gilpin and Thomas Sandholm. The solution is too lage to be given in text form, so the paper serves as an outline to the methods by which the authors derived their results. That's an issue for another article, though.

Rhode Island hold'em is played as follows:

  1. Each player antes.
  2. Each of two players is dealt one down card, followed by a round of betting.
  3. On each round of betting, the first player may check or bet. If player 1 checks, player 2 may check or bet. If one player bets, the other player may call or fold. If both players check or one player bets and the other calls, we proceed to the next round.
  4. Next, one shared board card is dealt, followed by a round of betting.
  5. Finally, one more board card is dealt, followed by a round of betting.
  6. If there is a showdown, the best hand wins, using the rules to the casino game "3-Card Poker" to determine a winner. That is, the order of hands is 3-card straight-flush, trips, 3-card straight, 3-card flush, pair, high card.

Alternatively, the rules to this game can be expressed as Texas hold'em with no flop and only one down card where the hands are evaluated according to the hand rankings for 3-Card Poker.

In the next section of the paper, instead of using binning, they reduce the complexity of finding a solution to a poker game by considering each round of a multi-round poker game to be independent. That is, instead of finding the solution to the complete game, we treat each betting round as an independent game.

Here's how this might work in "real life": A Texas hold'em hand is played by a team of four players. The first player plays the pre-flop betting round with none of these colleagues in the room or able to see the action in any way. After the pre-flop betting round is complete, that player leaves the room without communicating with anyone, and player two comes in to play the flop. This player knows the cards and the size of the pot, but does not know whether the first player bet or called to get there (assuming that the first player didn't fold.) After the flop betting round, the second player is replaced for the turn and the river in a similar manner.

This simplification preculdes adopting a multi-street betting strategy. However, it doesn't preclude any of the multi-street strategies with which poker players are familiar, it just means that they have to occur by "accident" rather than by a priori design. For example, a certain amount of time a player will bluff on the first round, as game theory suggests that they should, and, just by accident, a certain amount of time they will also bluff on the second and even the third betting rounds.

Thus, it is possible, albeit rare, for a player to make a three-barrel bluff, but since most players should agree that this strategy should be adopted rarely (unless one's opponent has demonstrated themselves to be ridiculously weak on the river), that doesn't seem to be a huge impediment. Similarly, slow-playing, semi-bluffing, floating, and other popular strategies can all occur "by accident" as well, however they might not occur as frequently or effectively as a strategy that considers the totality of all betting rounds. Nonetheless, they can all arise even if the betting rounds are considered independently. So, while there can be a significant disadvantage to treating betting rounds independently, for some types of games it's possible that such an abstraction may not be as horrendous as it first appears.

In any case, the authors demonstrate that by adopting this approximation they can save a great deal of compute time in evaluating positions in Rhode Island hold'em, although since they don't have an exact solution to this game (at least not at the time the paper was written), they don't know how much EV this abstraction method costs.

However, they can play their independent betting-round strategy against some sample programmed algorithms. One opponent is a "rule-based" player, that is, a player whose play is governed by a look-up table with a pre-programmed strategy that approximates what the authors think a good strategy for Rhode Island hold'em looks like. This is essentially the same method used by such programs as Wilson's Turbo Texas Hold'em. The second and third opponents are based on the rule-based strategy, skewed to be too aggressive and too passive respectively. The fourth opponent is one that implements an "opponent-modeling" method. That is, it analyzes opponent behavior and attempts to figure out its opponent's strategy and determine a proper counter-strategy. This is the approach taken by the Loki program developed by the University of Alberta research group. This algorithm can be found in the the "Poker Academy" and "STACKED" commercial computer software packages.

In any case, the independent betting model beat all four of these opponents. The normal rule-based model out-performed both the too aggressive and too passive models, and the opponent-modeling program beat all three rule-based models. Does this indicate that an independent model will do well against a better algorithm? No, but it does indicate that the notion of independent betting isn't a horrible idea. How bad it is remains to be seen, and it almost certainly depends on the structure of the game.

As I already mentioned, since the publication of this paper the game of Rhode Island hold'em has been solved. This means that the authors could now play their independent abstraction model against an opponent who plays optimally. From this we could see how much one actually loses treating each betting round independently. Such a study would be an excellent follow-up to this line of research.

One parenthetical comment the authors made that I found to be especially interesting is that the independent betting program used both bluffing and slow-playing strategies in its mix. I fully expected to find out that it would be bluffing, this has been mathematically known to be an important part of poker since the beginnings of game theory. However, I was a little surprised to find that a simplified strategy for a game with three betting rounds would include slow-playing. I don't think I've seen that in an algorithm before outside of a rule-based or opponent-modeling approach. I'm curious to find out of the complete solution to the game also includes slow-playing.

Conclusion

Of course, real poker is more complex than the games examined here, and it's not clear exactly how far we can generalize these results for real games. Nonetheless, the authors' point in that for some games it is possible to develop abstractions, namely binning and treating each betting round independently, and that under the right circumstances these abstractions still produce good strategies for poker-like games.