ADMI 2017: Difference between revisions

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|City=São Paulo
|City=São Paulo
|Country=Brazil
|Country=Brazil
|has general chair=Philip S. Yu, Longbing Cao
|has program chair=Yifeng Zeng, Yang Gao, Hao Wang, Yingke Chen
}}
}}
The Thirteenth International Workshop on Agents and Data Mining Interaction (ADMI-17), Sao Paulo, Brazil, May 8, 2017
The Thirteenth International Workshop on Agents and Data Mining Interaction (ADMI-17), Sao Paulo, Brazil, May 8, 2017
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Held in conjunction with Sixteenth International Joint Conference on AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS-2017)
Held in conjunction with Sixteenth International Joint Conference on AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS-2017)
http://www.aamas2017.org/
http://www.aamas2017.org/
==Topics==
* Data-driven decision making and planning
* Data-driven reasoning and learning
* Data-driven agent interaction and coordination
* Data-driven personalized agents
* Deep learning for agent systems
* Agent-based methods and tools for big data analysis
* Applications of data-driven agent systems

Latest revision as of 09:28, 26 August 2020

ADMI 2017
13th International Workshop on Agents and Data Mining Interaction
Event in series ADMI
Dates 2017/05/08 (iCal) - 2017/05/08
Homepage: http://admi17.agentmining.org/
Location
Location: São Paulo, Brazil
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Important dates
Submissions: 2017/02/05
Committees
General chairs: Philip S. Yu, Longbing Cao
PC chairs: Yifeng Zeng, Yang Gao, Hao Wang, Yingke Chen
Table of Contents


The Thirteenth International Workshop on Agents and Data Mining Interaction (ADMI-17), Sao Paulo, Brazil, May 8, 2017 http://admi17.agentmining.org/

Held in conjunction with Sixteenth International Joint Conference on AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS-2017) http://www.aamas2017.org/

Topics

  • Data-driven decision making and planning
  • Data-driven reasoning and learning
  • Data-driven agent interaction and coordination
  • Data-driven personalized agents
  • Deep learning for agent systems
  • Agent-based methods and tools for big data analysis
  • Applications of data-driven agent systems