AN ANALYSIS OF UCT IN MULTI-PLAYER GAMES

By Nathan R Sturtevant. 2008

133 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Game & Decision Theory, Algorithms & Complexity

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Sociology and Political Science

In Game & Decision Theory

In Algorithms & Complexity

Citations in the corpus

TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning (2017, background): “Traditionally, such methods have been limited to domains where perfect environment simulators are available, such as board or card games (Coulom, 2006; Sturtevant, 2008).”
Learning to Win by Reading Manuals in a Monte-Carlo Framework (2011, methodology): “Our method operates within the Monte-Carlo search framework (Tesauro and Galperin, 1996), which has been successfully applied to complex computer games such as Go, Poker, Scrabble, multi-player card games, and real-time strategy games, among others (Gelly et al., 2006; Tesauro and Galperin, 1996; Bi”
Monte-Carlo tree search and rapid action value estimation in computer Go (2011, unknown): “…for search, which has revolutionised computer Go [2] [3], and is rapidly replacing traditional search algorithms as the method of choice in challenging domains such as General Game Playing [4], Amazons [5], Lines of Action [6], multi-player card games [7] [8], and real-time strategy games [9].”
Temporal-difference search in computer Go (2012, unknown): “…in a variety of challenging games, including Go (Gelly et al. 2006), General Game Playing (Finnsson and Björnsson 2008), Amazons (Lorentz 2008), Lines of Action (Winands and Björnsson 2009), multi-player card games (Schäfer 2008; Sturtevant 2008), and real-time strategy games (Balla and Fern 2009).”
Deep Reinforcement Learning (2018, background): “Traditionally, such methods have been limited to domains where perfect environment simulators are available, such as board or card games (Coulom, 2006; Sturtevant, 2008).”
Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games (2015, methodology): “This approach has also performed well in other games like Scrabble [34], Hearts [35], and Skat [5].”
A Survey of Monte Carlo Tree Search Methods (2012, background): “Sturtevant [207] shows that this variant of UCT converges to an”
Computing Approximate Nash Equilibria and Robust Best-Responses Using Sampling (2011, unknown): “In games with imper- fect information, it has no convergence guarantees for finding Nash-Equilibria.3 However, it has been reported that it may nonetheless produce strong players in games with imperfect information (Sturtevant, 2008).”