Investigating the Limits of Monte-Carlo Tree Search Methods in Computer Go

2014 ◽  
pp. 39-48 ◽  
Author(s):  
Shih-Chieh Huang ◽  
Martin Müller
Keyword(s):  
Monte Carlo ◽  
Tree Search ◽  
Search Methods ◽  
Monte Carlo Tree Search ◽  
Computer Go

scholarly journals A Survey of Monte Carlo Tree Search Methods

2012 ◽  
Vol 4 (1) ◽  
pp. 1-43 ◽  
Author(s):  
Cameron B. Browne ◽  
Edward Powley ◽  
Daniel Whitehouse ◽  
Simon M. Lucas ◽  
Peter I. Cowling ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Tree Search ◽  
Search Methods ◽  
Monte Carlo Tree Search

scholarly journals Improving Monte Carlo Tree Search with Artificial Neural Networks without Heuristics

2021 ◽  
Vol 11 (5) ◽  
pp. 2056
Author(s):  
Alba Cotarelo ◽  
Vicente García-Díaz ◽  
Edward Rolando Núñez-Valdez ◽  
Cristian González García ◽  
Alberto Gómez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Tree Search ◽  
Specific Knowledge ◽  
Search Methods ◽  
Monte Carlo Tree Search ◽  
Artificial Neural

Monte Carlo Tree Search is one of the main search methods studied presently. It has demonstrated its efficiency in the resolution of many games such as Go or Settlers of Catan and other different problems. There are several optimizations of Monte Carlo, but most of them need heuristics or some domain language at some point, making very difficult its application to other problems. We propose a general and optimized implementation of Monte Carlo Tree Search using neural networks without extra knowledge of the problem. As an example of our proposal, we made use of the Dots and Boxes game. We tested it against other Monte Carlo system which implements specific knowledge for this problem. Our approach improves accuracy, reaching a winning rate of 81% over previous research but the generalization penalizes performance.

Application of Monte-Carlo Tree Search in Tsumego of Computer Go

2012 ◽  
Vol 239-240 ◽  
pp. 1344-1347
Author(s):  
Fang Wang ◽  
Ying Peng
Keyword(s):  
Monte Carlo ◽  
Domain Knowledge ◽  
Tree Search ◽  
Evaluation Function ◽  
Monte Carlo Tree Search ◽  
Computer Go ◽  
Monte Carlo Evaluation ◽  
Expert Input ◽  
Essential Problem

The Tsumego problem in Go is a basic and essential problem to be overcome in implementing a computer Go program. This paper proposed a reality of Monte-Carlo tree search in Tsumego of computer Go which using Monte-Carlo evaluation as an alternative for a positional evaluation function. The advantage of this technique is that it requires few domain knowledge or expert input.

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