We show how to find a normal form proper equilibrium in behavior strategies of a given two-player zero-sum extensive form game with imperfect information but perfect recall. Our algorithm solves a finite sequence of linear programs and runs in polynomial time. For the case of a perfect information game, we show how to find a normal form proper equilibrium in linear time by a simple backwards induction procedure.

Refining an algorithm due to Koller, Megiddo and von Stengel, we show how to apply Lemke's algorithm for solving linear complementarity programs to compute a quasi-perfect equilibrium in behavior strategies of a given two-player extensive-form game of perfect recall. A quasi-perfect equilibrium is known to be sequential, and our algorithm thus resolves a conjecture of McKelvey and McLennan in the positive. A quasi-perfect equilibrium is also known to be normal-form perfect and our algorithm thus provides an alternative to an algorithm by von Stengel, van den Elzen and Talman. For the case of a zero-sum game, we devise variants of the algorithm that rely on linear programming rather than linear complementarity programming and use the simplex algorithm or other algorithms for linear programming rather than Lemke's algorithm. We argue that these latter algorithms are relevant for recent applications of equilibrium computation to artificial intelligence.

Koller, Megiddo and von Stengel showed how to efficiently compute minimax strategies for two-player extensive-form zero-sum games with imperfect information but perfect recall using linear programming and avoiding conversion to normal form. Koller and Pfeffer pointed out that the strategies obtained by the algorithm are not necessarily sequentially rational and that this deficiency is often problematic for the practical applications. We show how to remove this deficiency by modifying the linear programs constructed by Koller, Megiddo and von Stengel so that pairs of strategies forming a sequential equilibrium are computed. In particular, we show that a sequential equilibrium for a two-player zero-sum game with imperfect information but perfect recall can be found in polynomial time. In addition, the equilibrium we find is normal-formperfect. Our technique generalizes to general-sum games, yielding an algorithm for such games which is likely to be prove practical, even though it is not polynomial-time.

We show that a proper equilibrium of a matrix game can be found in polynomial time by solving a linear (in the number of pure strategies of the two players) number of linear programs of roughly the same dimensions as the standard linear programs describing the Nash equilibria of the game.

We present Tartanian, a game theory-based player for heads-up no-limit Texas Hold'em poker. Tartanian is built from three components. First, to deal with the virtually infinite strategy space of no-limit poker, we develop a discretized betting model designed to capture the most important strategic choices in the game. Second, we employ potential-aware automated abstraction algorithms for identifying strategically similar situations in order to decrease the size of the game tree. Third, we develop a new technique for automatically generating the source code of an equilibrium-finding algorithm from an XML-based description of a game. This automatically generated program is more efficient than what would be possible with a general-purpose equilibrium-finding program. Finally, we present results from the AAAI-07 Computer Poker Competition, in which Tartanian placed second out of ten entries.

We present a new abstraction algorithm for sequential imperfect information games. While most prior abstraction algorithms employ a myopic expected-value computation as a similarity metric, our algorithm considers a higherdimensional space consisting of histograms over abstracted classes of states from later stages of the game. This enables our bottom-up abstraction algorithm to automatically take into account potential: a hand can become relatively better (or worse) over time and the strength of different hands can get resolved earlier or later in the game. We further improve the abstraction quality by making multiple passes over the abstraction, enabling the algorithm to narrow the scope of analysis to information that is relevant given abstraction decisions made for earlier parts of the game. We also present a custom indexing scheme based on suit isomorphisms that enables one to work on significantly larger models than before. We apply the techniques to heads-up limit Texas Hold'em poker. Whereas all prior game theory-based work for Texas Hold'em poker used generic off-the-shelf linear program solvers for the equilibrium analysis of the abstracted game, we make use of a recently developed algorithm based on the excessive gap technique from convex optimization. This paper is, to our knowledge, the first to abstract and gametheoretically analyze all four betting rounds in one run (rather than splitting the game into phases). The resulting player, GS3, beats BluffBot, GS2, Hyperborean, Monash-BPP, Sparbot, Teddy, and Vexbot, each with statistical significance. To our knowledge, those competitors are the best prior programs for the game.

We analyze a heads-up no-limit Texas Hold'em poker tournament with a fixed small blind of 300 chips, a fixed big blind of 600 chips and a total amount of 8000 chips on the table (until recently, these parameters defined the headsup endgame of sit-n-go tournaments on the popular Party- Poker.com online poker site). Due to the size of this game, a computation of an optimal (i.e. minimax) strategy for the game is completely infeasible. However, combining an algorithm due to Koller, Megiddo and von Stengel with concepts of Everett and suggestions of Sklansky, we compute an optimal jam/fold strategy, i.e. a strategy that would be optimal if any bet made by the player playing by the strategy (but not bets of his opponent) had to be his entire stack. Our computations establish that the computed strategy is nearoptimal for the unrestricted tournament (i.e., with post-flop play being allowed) in the rigorous sense that a player playing by the computed strategy will win the tournament with a probability within 1.4 percentage points of the probability that an optimal strategy (allowing post-flop play) would give.

We consider approximating the minmax value of a multi-player game in strategic form. Tightening recent bounds by Borgs et al., we observe that approximating the value with a precision of ε log n digits (for any constant ε>0) is NP-hard, where n is the size of the game. On the other hand, approximating the value with a precision of c log log n digits (for any constant c≥1) can be done in quasi-polynomial time. We consider the parameterized complexity of the problem, with the parameter being the number of pure strategies k of the player for which the minmax value is computed. We show that if there are three players, k=2 and there are only two possible rational payoffs, the minmax value is a rational number and can be computed exactly in linear time. In the general case, we show that the value can be approximated with any polynomial number of digits of accuracy in time n^O(k). On the other hand, we show that minmax value approximation is W[1]-hard and hence not likely to be fixed parameter tractable. Concretely, we show that if k-CLIQUE requires time n^Ω(k) then so does minmax value computation.

At AAAI'07, Zinkevich, Bowling and Burch introduced the Range of Skill measure of a two-player game and used it as a parameter in the analysis of the running time of an algorithm for finding approximate solutions to such games. They suggested that the Range of Skill of a typical natural game is a small number, but only gave heuristic arguments for this. In this paper, we provide the first methods for rigorously estimating the Range of Skill of a given game. We provide some general, asymptotic bounds that imply that the Range of Skill of a perfectly balanced game tree is almost exponential in its size (and doubly exponential in its depth). We also provide techniques that yield concrete bounds for unbalanced game trees and apply these to estimate the Range of Skill of Tic-Tac-Toe and Heads-Up Limit Texas Hold'em Poker. In particular, we show that the Range of Skill of Tic-Tac-Toe is more than 100,000.

We revisit the deterministic graphical games of Washburn. A deterministic graphical game can be described as a simple stochastic game (a notion due to Anne Condon), except that we allow arbitrary real payoffs but disallow moves of chance. We study the complexity of solving deterministic graphical games and obtain an almost-linear time comparison-based algorithm for computing an equilibrium of such a game. The existence of a linear time comparison-based algorithm remains an open problem.

We consider finding maximin strategies and equilibria of explicitly given extensive form games with imperfect information but with no moves of chance. We show that a maximin pure strategy for a two- player game with perfect recall and no moves of chance can be found in time linear in the size of the game tree and that all pure Nash equilibrium outcomes of a two-player general-sum game with perfect recall and no moves of chance can be enumerated in time linear in the size of the game tree. We also show that finding an optimal behavior strategy for a one-player game of no chance without perfect recall and determining whether an equilibrium in behavior strategies exists in a two-player zero-sum game of no chance without perfect recall are both NP-hard.

We demonstrate two game theory-based programs for headsup limit and no-limit Texas Hold'em poker. The first player, GS3, is designed for playing limit Texas Hold'em, in which all bets are a fixed amount. The second player, Tartanian, is designed for the no-limit variant of the game, in which the amount bet can be any amount up to the number of chips the player has. Both GS3 and Tartanian are based on our potential-aware automated abstraction algorithm for identifying strategically similar situations in order to decrease the size of the game tree. Tartanian, in order to deal with the virtually infinite strategy space of no-limit poker, in addition uses a discretized betting model designed to capture the most important strategic choices in the game. The strategies for both players are computed using our improved version of Nesterov's excessive gap technique specialized for poker. In this demonstration, participants will be invited to play against both of the players, and to experience first-hand the sophisticated strategies employed by our players.