Analysis of Coalition Formation in Cooperative Games Using Crowdsourcing and Machine Learning

Yuko Sakurai, Satoshi Oyama

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

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.

本文言語英語
ホスト出版物のタイトルAI 2019
ホスト出版物のサブタイトルAdvances in Artificial Intelligence - 32nd Australasian Joint Conference, 2019, Proceedings
編集者Jixue Liu, James Bailey
出版社Springer
ページ78-88
ページ数11
ISBN(印刷版)9783030352875
DOI
出版ステータス出版済み - 2019
外部発表はい
イベント32nd Australasian Joint Conference on Artificial Intelligence, AI 2019 - Adelaide, オーストラリア
継続期間: 12 2 201912 5 2019

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11919 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議32nd Australasian Joint Conference on Artificial Intelligence, AI 2019
国/地域オーストラリア
CityAdelaide
Period12/2/1912/5/19

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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