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ORCID1st DAI 2019: Beijing, China
- Proceedings of the First International Conference on Distributed Artificial Intelligence, DAI 2019, Beijing, China, October 13-15, 2019. ACM 2019, ISBN 978-1-4503-7656-3
- Feng Wu
, Shlomo Zilberstein, Nicholas R. Jennings:
Stochastic multi-agent planning with partial state models. 1:1-1:8 - Poom Pianpak, Tran Cao Son, Phoebe O. Toups Dugas, William Yeoh:
A distributed solver for multi-agent path finding problems. 2:1-2:7 - Sharadhi Alape Suryanarayana, David Sarne, Sarit Kraus:
Information disclosure and partner management in affiliate marketing. 3:1-3:8 - Matthieu Zimmer, Paul Weng:
An efficient reinforcement learning algorithm for learning deterministic policies in continuous domains. 4:1-4:7 - Michael Gershtein, David Sarne, Yonatan Aumann:
Approval voting with costly information. 5:1-5:8 - Weijun Hong, Menghui Zhu, Minghuan Liu, Weinan Zhang, Ming Zhou, Yong Yu, Peng Sun:
Generative adversarial exploration for reinforcement learning. 6:1-6:10 - Ming Zhou, Yong Chen, Ying Wen, Yaodong Yang, Yufeng Su, Weinan Zhang, Dell Zhang, Jun Wang:
Factorized Q-learning for large-scale multi-agent systems. 7:1-7:7 - Junping Xu, Xin He, Dengji Zhao:
Double auction design on networks. 8:1-8:6 - Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Yang Yu:
Asynchronous classification-based optimization. 9:1-9:8 - Sam Ganzfried:
Mistakes in games. 10:1-10:6 - Weixun Wang, Jianye Hao, Yixi Wang, Matthew E. Taylor:
Achieving cooperation through deep multiagent reinforcement learning in sequential prisoner's dilemmas. 11:1-11:7 - Jingchang Sun, Yulong Zeng:
Moral hazard in games of miners. 12:1-12:2
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