Computer poker: A review

By Jonathan Rubin, Ian D. Watson. 2011

115 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Reinforcement Learning

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Citations in the corpus

OpenSpiel: A Framework for Reinforcement Learning in Games (2019, background): “It has revolutionized Poker AI research [58, 60], leading to the largest variants of poker being solved and competitive polices that have beat top human professionals [10, 45, 13, 15].”
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (2019, methodology): “It has also motivated MARL algorithms that revolutionized competitive setting applications like Poker AI (Rubin and Watson, 2011; Brown and Sandholm, 2019).”
Opponent Modeling by Expectation–Maximization and Sequence Prediction in Simplified Poker (2017, unknown): “For more information we refer the reader to the review by Sandholm on the state of solving incomplete-information games [22], and the review by Rubin and Watson on algorithms, approaches , and agents in computer poker [23].”
Self-play Monte-Carlo tree search in computer poker (2014, background): “Computer poker provides a diversity of stochastic imperfect information games of different sizes and has proved to be a fruitful research domain for game theory and artificial intelligence (Sandholm 2010; Rubin and Watson 2011).”
A Survey of Monte Carlo Tree Search Methods (2012, background): “Coquelin and Munos [68] demonstrate that ﬂat UCB retains the adaptivity of standard UCT while improving its regret bounds in certain worst cases where UCT is overly optimistic.”
A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft (2013, unknown): “— Adversarial planning under uncertainty of partially observable domains: How can we adapt adversarial planning techniques for dealing with uncertainty? This problem has been widely studied in the context of simple games such as Back-Gammon [70], or Poker [71].”
Autonomous agents modelling other agents: A comprehensive survey and open problems (2017, background): “…other agents has been an active area of research in many sub-communities, including classic game playing (F¨urnkranz, 2001), computer Poker (Rubin and Watson, 2011), automated negotiation (Baarslag et al., 2016), simulated robot soccer (Kitano et al., 1997), human user modelling (Zukerman…”
Evaluating and modelling Hanabi-playing agents (2017, background): “…agents feature strongly in this, as do simulation-based agents such as MCTS. Poker has been extensively studied — see the review conducted by Rubin & Watson [13]; one of their notable ﬁnds was that a simulation-based approach is inferior to the formula-based approach, despite expectations.”
Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games (2015, methodology): “This oﬄine approach has been remarkably successful in Computer Poker [2, 12, 32, 31, 21].”
Learning Policy Representations in Multiagent Systems (2018, background): “In competitive domains such as Poker and Go, such tasks are often integrated with domain-speciﬁc heuristics to model opponents and learn superior policies (Rubin & Watson, 2011; Mnih et al., 2015).”
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent (2019, methodology): “The challenge domain for imperfect information has been computer Poker, which has driven much of the progress in computational approaches to equilibrium-ﬁnding [Rubin and Watson, 2011].”