Human-agents Interactions in Multi-Agent Systems: A case study of human-UAVs team for forest fire lookouts

Sagir Muhammad Yusuf, Chris Baber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose an architecture that uses predictions tools obtained via Bayesian learning algorithms to monitor the issues of communication, fault tolerance, and adaptation in human-agent mission. The architecture describes different level of knowledge, planning, and commands differ by their priorities. We tested the model using forest fire lookouts problem on a simulation platform (AMASE). The process uses the conjugate gradient descent algorithm to perform the Bayesian Belief Network training. The output of the training process is a well-trained BBN for agents’ prediction, estimation, and decision making during communication failure. The prediction perfection of the human and agents were compared and studied. Although results proof that human approach is prone to error but is good in terms of emergency commands execution. We suggested that the use of a well-trained prediction tool (i.e., the output BBN) could be used in monitoring mission during communication link, hardware, or software breakdown.

Original languageEnglish
Title of host publicationICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
EditorsAna Rocha, Luc Steels, Jaap van den Herik
PublisherSciTePress
Pages347-354
Number of pages8
ISBN (Electronic)9789897583957
Publication statusPublished - 2020
Event12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta
Duration: 22 Feb 202024 Feb 2020

Publication series

NameICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
Volume1

Conference

Conference12th International Conference on Agents and Artificial Intelligence, ICAART 2020
Country/TerritoryMalta
CityValletta
Period22/02/2024/02/20

Bibliographical note

Publisher Copyright:
Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Keywords

  • Bayesian Learning
  • Human-agent Team
  • Mixed-initiative Planning
  • Mixed-initiative Reasoning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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