By the same authors

Monte Carlo Tree Search

Research output: Non-textual formSoftware

Standard

Monte Carlo Tree Search. Cowling, Peter I (Developer); Powley, Edward Jack (Developer); Whitehouse, Daniel (Developer). 2012.

Research output: Non-textual formSoftware

Harvard

Cowling, PI, Powley, EJ & Whitehouse, D, Monte Carlo Tree Search, 2012, Software.

APA

Cowling, P. I. (Developer), Powley, E. J. (Developer), & Whitehouse, D. (Developer). (2012). Monte Carlo Tree Search. Software, Retrieved from http://mcts.ai/

Vancouver

Cowling PI (Developer), Powley EJ (Developer), Whitehouse D (Developer). Monte Carlo Tree Search 2012.

Author

Cowling, Peter I (Developer) ; Powley, Edward Jack (Developer) ; Whitehouse, Daniel (Developer). / Monte Carlo Tree Search. [Software].

Bibtex - Download

@misc{af958136c11b440f8cbb632abb73a6d2,
title = "Monte Carlo Tree Search",
abstract = "Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial intelligence (AI) problems, typically move planning in combinatorial games. It combines the generality of random simulation with the precision of tree search.Research interest in MCTS has risen sharply due to its spectacular success with computer Go and potential application to a number of other difficult problems. Its application extends beyond games, and MCTS can theoretically be applied to any domain that can be described in terms of {state, action} pairs and simulation used to forecast outcomes.",
author = "Cowling, {Peter I} and Powley, {Edward Jack} and Daniel Whitehouse",
year = "2012",
language = "English",

}

RIS (suitable for import to EndNote) - Download

TY - ADVS

T1 - Monte Carlo Tree Search

A2 - Cowling, Peter I

A2 - Powley, Edward Jack

A2 - Whitehouse, Daniel

PY - 2012

Y1 - 2012

N2 - Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial intelligence (AI) problems, typically move planning in combinatorial games. It combines the generality of random simulation with the precision of tree search.Research interest in MCTS has risen sharply due to its spectacular success with computer Go and potential application to a number of other difficult problems. Its application extends beyond games, and MCTS can theoretically be applied to any domain that can be described in terms of {state, action} pairs and simulation used to forecast outcomes.

AB - Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial intelligence (AI) problems, typically move planning in combinatorial games. It combines the generality of random simulation with the precision of tree search.Research interest in MCTS has risen sharply due to its spectacular success with computer Go and potential application to a number of other difficult problems. Its application extends beyond games, and MCTS can theoretically be applied to any domain that can be described in terms of {state, action} pairs and simulation used to forecast outcomes.

M3 - Software

ER -