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» Monte-Carlo Tree Search: A New Framework for Game AI
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CG
2006
Springer
13 years 11 months ago
Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search
Monte-Carlo evaluation consists in estimating a position by averaging the outcome of several random continuations, and can serve as an evaluation function at the leaves of a min-ma...
Rémi Coulom
JAIR
2011
187views more  JAIR 2011»
13 years 2 months ago
A Monte-Carlo AIXI Approximation
This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two ke...
Joel Veness, Kee Siong Ng, Marcus Hutter, William ...
ICCV
2009
IEEE
13 years 5 months ago
Efficient human pose estimation via parsing a tree structure based human model
Human pose estimation is the task of determining the states (location, orientation and scale) of each body part. It is important for many vision understanding applications, e.g. v...
Xiaoqin Zhang, Changcheng Li, Xiaofeng Tong, Weimi...
GAMEON
2001
13 years 9 months ago
A New Computational Approach to the Game of Go
This paper investigates the application of neural network techniques to the creation of a program that can play the game of Go with some degree of success. The combination of soft...
Julian Churchill, Richard Cant, David Al-Dabass
AI
2002
Springer
13 years 7 months ago
Improving heuristic mini-max search by supervised learning
This article surveys three techniques for enhancing heuristic game-tree search pioneered in the author's Othello program Logistello, which dominated the computer Othello scen...
Michael Buro