%0 Conference Paper %1 ohto2017curling %A Ohto, Katsuki %A Tanaka, Tetsuro %B ACG %D 2017 %E Winands, Mark H. M. %E van den Herik, H. Jaap %E Kosters, Walter A. %I Springer %K Computersimulation Computerspiel Curling Strategie Taktik %P 151-164 %R 10.1007/978-3-319-71649-7_13 %T A Curling Agent Based on the Monte-Carlo Tree Search Considering the Similarity of the Best Action Among Similar States %U http://dblp.uni-trier.de/db/conf/acg/acg2017.html#OhtoT17 %V 10664 %X Curling is one of the most strategic winter sports. Recently, many computer scientists have studied curling strategies. The Digital Curling system is a framework used to compare curling strategies. Herein, we present a computer agent based on the Monte-Carlo Tree Search (MCTS) for the Digital Curling framework. We implemented a novel action decision method based on MCTS for Markov decision processes with continuous state space. The experimental results show that our search method is effective for agents with a simple simulation policy and agents with a handmade complex one.

@inproceedings{ohto2017curling, abstract = {Curling is one of the most strategic winter sports. Recently, many computer scientists have studied curling strategies. The Digital Curling system is a framework used to compare curling strategies. Herein, we present a computer agent based on the Monte-Carlo Tree Search (MCTS) for the Digital Curling framework. We implemented a novel action decision method based on MCTS for Markov decision processes with continuous state space. The experimental results show that our search method is effective for agents with a simple simulation policy and agents with a handmade complex one.}, added-at = {2019-05-17T15:50:38.000+0200}, author = {Ohto, Katsuki and Tanaka, Tetsuro}, biburl = {https://www.bibsonomy.org/bibtex/2602cd0018a70be2fd9afe12f963c33e6/cckonstanz}, booktitle = {ACG}, doi = {10.1007/978-3-319-71649-7_13}, editor = {Winands, Mark H. M. and van den Herik, H. Jaap and Kosters, Walter A.}, interhash = {7350d335835356bd0b3b52f54111da5e}, intrahash = {602cd0018a70be2fd9afe12f963c33e6}, keywords = {Computersimulation Computerspiel Curling Strategie Taktik}, pages = {151-164}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2019-05-20T10:10:51.000+0200}, title = {A Curling Agent Based on the Monte-Carlo Tree Search Considering the Similarity of the Best Action Among Similar States}, url = {http://dblp.uni-trier.de/db/conf/acg/acg2017.html#OhtoT17}, volume = 10664, year = 2017 }