Human-agents Interactions in Multi-Agent Systems: A Case Study of Human-UAVs Team for Forest Fire Lookouts

Sagir Yusuf, Chris Baber

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.

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Paper Citation


in Harvard Style

Yusuf S. and Baber C. (2020). Human-agents Interactions in Multi-Agent Systems: A Case Study of Human-UAVs Team for Forest Fire Lookouts.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: HAMT, ISBN 978-989-758-395-7, pages 347-354. DOI: 10.5220/0009369203470354


in Bibtex Style

@conference{hamt20,
author={Sagir Yusuf and Chris Baber},
title={Human-agents Interactions in Multi-Agent Systems: A Case Study of Human-UAVs Team for Forest Fire Lookouts},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: HAMT,},
year={2020},
pages={347-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009369203470354},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: HAMT,
TI - Human-agents Interactions in Multi-Agent Systems: A Case Study of Human-UAVs Team for Forest Fire Lookouts
SN - 978-989-758-395-7
AU - Yusuf S.
AU - Baber C.
PY - 2020
SP - 347
EP - 354
DO - 10.5220/0009369203470354