Incentive Learning in Monte Carlo Tree Search

Monte Carlo tree search (MCTS) is a search paradigm that has been remarkably successful in computer games like Go. It uses Monte Carlo simulation to evaluate the values of nodes in a search tree. The node values are then used to select the actions during subsequent simulations. The performance of MC...

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Veröffentlicht in:	IEEE transactions on computational intelligence and AI in games. 2013-12, Vol.5 (4), p.346-352
Hauptverfasser:	Kao, Kuo-Yuan, Wu, I-Chen, Yen, Shi-Jim, Shan, Yi-Chang
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence combinatorial games computational intelligence computer games Computers Game theory Games Monte Carlo methods Radiation detectors reinforcement learning Size measurement Temperature measurement
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