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Scalable Heterogeneous Multiagent Learning from Demonstration

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Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection (PAAMS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12092))

Abstract

We present a method of supervised learning from demonstration for real-time, online training of complex heterogenous multiagent behaviors which scale to large numbers of agents in operation. Our learning method is applicable in domains where coordinated behaviors must be created quickly in unexplored environments. Examples of such problem domains includes disaster relief, search and rescue, and gaming environments. We demonstrate this training method in an adversarial mining scenario which coordinates four types of individual agents to perform six distinct roles in a mining task.

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Authors and Affiliations

  1. Department of Computer Science, George Mason University, 4400 University Dr, Fairfax, VA, 22030, USA

    William Squires & Sean Luke

Authors
  1. William Squires
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  2. Sean Luke
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Corresponding author

Correspondence to William Squires .

Editor information

Editors and Affiliations

  1. Centre National de la Recherche Scientifique, Grenoble, France

    Yves Demazeau

  2. Catholic University of Leuven, Heverlee, Belgium

    Tom Holvoet

  3. University of Salamanca, Salamanca, Spain

    Juan M. Corchado

  4. University of L’Aquila, L'Aquila, Italy

    Stefania Costantini

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Squires, W., Luke, S. (2020). Scalable Heterogeneous Multiagent Learning from Demonstration. In: Demazeau, Y., Holvoet, T., Corchado, J., Costantini, S. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. PAAMS 2020. Lecture Notes in Computer Science(), vol 12092. Springer, Cham. https://doi.org/10.1007/978-3-030-49778-1_21

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  • DOI: https://doi.org/10.1007/978-3-030-49778-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49777-4

  • Online ISBN: 978-3-030-49778-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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