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  • Conference proceedings
  • © 2020

Distributed Artificial Intelligence

Second International Conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings

Editors:

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12547)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Conference series link(s): DAI: International Conference on Distributed Artificial Intelligence

Conference proceedings info: DAI 2020.

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  • ISBN: 978-3-030-64096-5
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Table of contents (9 papers)

  1. Front Matter

    Pages i-ix
  2. MGHRL: Meta Goal-Generation for Hierarchical Reinforcement Learning

    • Haotian Fu, Hongyao Tang, Jianye Hao, Wulong Liu, Chen Chen
    Pages 29-39
  3. Hybrid Independent Learning in Cooperative Markov Games

    • Roi Yehoshua, Christopher Amato
    Pages 69-84
  4. Efficient Exploration by Novelty-Pursuit

    • Ziniu Li, Xiong-Hui Chen
    Pages 85-102
  5. Battery Management for Automated Warehouses via Deep Reinforcement Learning

    • Yanchen Deng, Bo An, Zongmin Qiu, Liuxi Li, Yong Wang, Yinghui Xu
    Pages 126-139
  6. Back Matter

    Pages 141-141

Other Volumes

  1. Distributed Artificial Intelligence

About this book

This book constitutes the refereed proceedings of the Second International Conference on Distributed Artificial Intelligence, DAI 2020, held in Nanjing, China, in October 2020.

The 9 full papers presented in this book were carefully reviewed and selected from 22 submissions. DAI aims at bringing together international researchers and practitioners in related areas including general AI, multiagent systems, distributed learning, computational game theory, etc., to provide a single, high-profile, internationally renowned forum for research in the theory and practice of distributed AI.

Due to the Corona pandemic this event was held virtually.

Keywords

  • artificial intelligence
  • autonomous agents
  • computer vision
  • correlation analysis
  • distributed computer systems
  • education
  • engineering
  • intelligent agents
  • learning
  • machine learning
  • markov decision processes
  • markov processes
  • mathematics
  • multi-agent
  • multiagent system
  • probability
  • reinforcement learning
  • robotics
  • signal processing
  • software agents

Editors and Affiliations

  • University of Alberta, Edmonton, Canada

    Matthew E. Taylor

  • Nanjing University, Nanjing, China

    Yang Yu, Yang Gao

  • University of Oxford, Oxford, UK

    Edith Elkind

Bibliographic Information

Buying options

eBook USD 59.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-64096-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 74.99
Price excludes VAT (USA)