TY - GEN
T1 - Analysis of Coalition Formation in Cooperative Games Using Crowdsourcing and Machine Learning
AU - Sakurai, Yuko
AU - Oyama, Satoshi
N1 - Funding Information:
Acknowledgement. This work was partially supported by JSPS KAKENHI Grants JP17KK0008, 18H03301, and JP18H03337, by the Kayamori Foundation of Informational Science Advancement and by the Telecommunications Advancement Foundation.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Analysis of coalition formation in cooperative games is an important research topic in game theory. Previous studies on coalition formation used laboratory experiments to collect data on player decision making, but the amount of data collected was limited due to the high cost of laboratory experiments. In this study, we used crowdsourcing to collect a large volume of decision-making data for use in predicting player behavior in cooperative games. This large amount of data enabled us to train large machine learning models such as deep neural networks, which can more precisely predict player decision making in cooperative games. The results with our machine learning models using crowdsourced data were similar to those of laboratory experiments.
AB - Analysis of coalition formation in cooperative games is an important research topic in game theory. Previous studies on coalition formation used laboratory experiments to collect data on player decision making, but the amount of data collected was limited due to the high cost of laboratory experiments. In this study, we used crowdsourcing to collect a large volume of decision-making data for use in predicting player behavior in cooperative games. This large amount of data enabled us to train large machine learning models such as deep neural networks, which can more precisely predict player decision making in cooperative games. The results with our machine learning models using crowdsourced data were similar to those of laboratory experiments.
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U2 - 10.1007/978-3-030-35288-2_7
DO - 10.1007/978-3-030-35288-2_7
M3 - Conference contribution
AN - SCOPUS:85076558392
SN - 9783030352875
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 78
EP - 88
BT - AI 2019
A2 - Liu, Jixue
A2 - Bailey, James
PB - Springer
T2 - 32nd Australasian Joint Conference on Artificial Intelligence, AI 2019
Y2 - 2 December 2019 through 5 December 2019
ER -