The Annual Computer Poker Competition

Bard, Nolan; Hawkin, John; Rubin, Jonathan; Zinkevich, Martin

doi:10.1609/aimag.v34i2.2474

Cited by 15 publications

(10 citation statements)

References 4 publications

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“…It likely will not be possible to find a four-round game with a full deck and large stack sizes that remains both tractable and interesting; instead, we will have to simplify the game in some way. As motivation, we can consider the [2-1], [2][3][4], and [3-1] parameterized limit hold'em games recently proposed by Johanson et al [7], in which the number of rounds and maximum number of bets per round, respectively, are varied to produce smaller games. In the no-limit domain, the equivalent parameterization is a [r-$s] game, where r is the number of rounds and $s is the stack size.…”

Section: Discussionmentioning

confidence: 99%

“…Poker has proved popular for this task because it is a canonical example of a game with imperfect information and stochastic outcomes. Since 2006, the Annual Computer Poker Competition (ACPC) [12,2] has served as a venue for researchers to play their poker agents against each other, revealing which artificial intelligence techniques are effective in practice. The competition has driven research in the field of computational game theory, resulting in algorithms capable of finding close approximations to optimal strategies in ever larger games.…”

Section: Introductionmentioning

confidence: 99%

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Measuring the Size of Large No-Limit Poker Games

Johanson¹

2013

Preprint

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In the field of computational game theory, games are often compared in terms of their size. This can be measured in several ways, including the number of unique game states, the number of decision points, and the total number of legal actions over all decision points. These numbers are either known or estimated for a wide range of classic games such as chess and checkers. In the stochastic and imperfect information game of poker, these sizes are easily computed in "limit" games which restrict the players' available actions, but until now had only been estimated for the more complicated "no-limit" variants. In this paper, we describe a simple algorithm for quickly computing the size of two-player no-limit poker games, provide an implementation of this algorithm, and present for the first time precise counts of the number of game states, information sets, actions and terminal nodes in the no-limit poker games played in the Annual Computer Poker Competition.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring the Size of Large No-Limit Poker Games

Johanson¹

2013

Preprint

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“…While the rule-based approach provides a simple framework for implementing Texas Hold'em AIs, the resulting handcrafted strategies are easily exploitable by observant opponents. Since 2006, the annual computer poker competition (Bard et al 2013) has greatly facilitated poker AI development, and many game-theoretic Texas Hold'em AIs are proposed (Jackson 2013;Brown, Ganzfried, and Sandholm 2015). These systems first use various abstraction strategies (Johanson et al 2013;Ganzfried and Sandholm 2014) to merge similar game states to reduce the game size, then exploit some equilibriumfinding algorithms (e.g., CFR (Zinkevich et al 2008) and MCCFR (Lanctot et al 2009;Schmid et al 2019)) to find the approximate Nash equilibrium strategies which are robust to different opponents.…”

Section: Related Workmentioning

confidence: 99%

“…Duplicate Poker is a simple variance reduction technique that attempts to mitigate the effects of luck and is widely used in the annual computer poker competitions (Bard et al 2013). For example, in HUNL, let us say agent A plays one seat and agent B plays the other seat.…”

Section: Evaluation Protocolsmentioning

confidence: 99%

OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research

Li¹,

Xu²,

Zhao³

et al. 2020

Preprint

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Owning to the unremitting efforts by a few institutes, significant progress has recently been made in designing superhuman AIs in No-limit Texas Hold'em (NLTH), the primary testbed for large-scale imperfect-information game research. However, it remains challenging for new researchers to study this problem since there are no standard benchmarks for comparing with existing methods, which seriously hinders further developments in this research area. In this work, we present OpenHoldem, an integrated toolkit for large-scale imperfectinformation game research using NLTH. OpenHoldem makes three main contributions to this research direction: 1) a standardized evaluation protocol for thoroughly evaluating different NLTH AIs, 2) three publicly available strong baselines for NLTH AI, and 3) an online testing platform with easy-touse APIs for public NLTH AI evaluation. We have released OpenHoldem at http:// holdem.ia.ac.cn/ 1 , hoping it facilitates further studies on the unsolved theoretical and computational issues in this area and cultivate crucial research problems like opponent modeling, large-scale equilibrium-finding, and human-computer interactive learning.

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“…monitoring erratic betting behaviour by quantitative methods) could be useful for tilt-detection, but very difficult for current artificial intelligence (AI), although tremendous progress in poker AI research has been achieved by leading teams at the University of Alberta and Carnegie Mellon University [1]. Moreover, these teams have not specifically studied tilting at all.…”

Section: Related Workmentioning

confidence: 99%

The Science and Detection of Tilting

Wei

Palomäki

Yan

et al. 2016

Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval

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Tilting refers to losing control due to negative emotions, behaving erratically, and thereby suffering adverse consequences.The term tilt originated in poker playing communities, where it is often a consequence of so called bad beats, i.e. losing with an unlikely-to-lose poker hand. Often resulting in devastating monetary losses, tilting is ubiquitous and well known in poker, but rarely studied despite its significance. In this paper, we argue that tilting is a fertile topic for interdisciplinary research both for psychologists and computer scientists. Specifically, we propose to study the manifestation of tilting via facial emotion expressions, and motivate the development of an automatic tilt-detection system. The scientific understanding of the psychology of tilting can be increased via a computing approach, which has not been previously attempted. Automatic tilting detection will lead to a practical technology that reduces poker players' monetary losses and improves their well being through reduced tilting. We also argue that while tilting is best known as a poker phenomenon, it also exists in other contexts. Thus, the idea we suggest is a novel application of computer vision, affective computing and multimedia technologies in the real world, across many domains.

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The Annual Computer Poker Competition

Cited by 15 publications

References 4 publications

Measuring the Size of Large No-Limit Poker Games

Measuring the Size of Large No-Limit Poker Games

OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research

The Science and Detection of Tilting

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