Distributed situation awareness for multi-agent mission in dynamic environments: a case study of multi-UAVs wildfires searching

Sagir Muhammad Yusuf; Chris Baber

Distributed situation awareness for multi-agent mission in dynamic environments: a case study of multi-UAVs wildfires searching

Sagir Muhammad Yusuf
, Chris Baber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This thesis focuses attention on achieving Distributed Situation Awareness (DSA) with minimal resources (energy, processing cost, etc.) using small low-capacity agents (e.g., UAVs) coordinated in a decentralised fashion while conducting searching activity. This is in contrast to the existing works involving convoluted communication and information processing.

Original language	English
Title of host publication	The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)
Publisher	Association for the Advancement of Artificial Intelligence
Pages	15740-15741
Number of pages	2
ISBN (Print)	9781713835974
Publication status	Published - 28 May 2021
Event	35th AAAI Conference on Artificial Intelligence - Virtual, Online Duration: 2 Feb 2021 → 9 Feb 2021 https://aaai.org/Conferences/AAAI-21/

Publication series

Name	Proceedings of the AAAI Conference on Artificial Intelligence
Publisher	AAAI
Number	18
Volume	35
ISSN (Print)	2159-5399
ISSN (Electronic)	2374-3468

Conference

Conference	35th AAAI Conference on Artificial Intelligence
Abbreviated title	AAAI-2021
Period	2/02/21 → 9/02/21
Internet address	https://aaai.org/Conferences/AAAI-21/

Bibliographical note

Publisher Copyright:
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved

Keywords

Multi-agent System
Multi-agent Learning
Situation Awareness
Optimisation

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

https://ojs.aaai.org/index.php/AAAI/article/view/17867Licence: None: All rights reserved

Cite this

Yusuf, S. M., & Baber, C. (2021). Distributed situation awareness for multi-agent mission in dynamic environments: a case study of multi-UAVs wildfires searching. In The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) (pp. 15740-15741). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 35, No. 18). Association for the Advancement of Artificial Intelligence. https://ojs.aaai.org/index.php/AAAI/article/view/17867

Yusuf, Sagir Muhammad ; Baber, Chris. / Distributed situation awareness for multi-agent mission in dynamic environments : a case study of multi-UAVs wildfires searching. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21). Association for the Advancement of Artificial Intelligence, 2021. pp. 15740-15741 (Proceedings of the AAAI Conference on Artificial Intelligence; 18).

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title = "Distributed situation awareness for multi-agent mission in dynamic environments: a case study of multi-UAVs wildfires searching",

abstract = "This thesis focuses attention on achieving Distributed Situation Awareness (DSA) with minimal resources (energy, processing cost, etc.) using small low-capacity agents (e.g., UAVs) coordinated in a decentralised fashion while conducting searching activity. This is in contrast to the existing works involving convoluted communication and information processing.",

keywords = "Multi-agent System, Multi-agent Learning, Situation Awareness, Optimisation",

author = "Yusuf, \{Sagir Muhammad\} and Chris Baber",

note = "Publisher Copyright: Copyright {\textcopyright} 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved; 35th AAAI Conference on Artificial Intelligence, AAAI-2021 ; Conference date: 02-02-2021 Through 09-02-2021",

year = "2021",

month = may,

day = "28",

language = "English",

isbn = "9781713835974",

series = "Proceedings of the AAAI Conference on Artificial Intelligence",

publisher = "Association for the Advancement of Artificial Intelligence",

number = "18",

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booktitle = "The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)",

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Yusuf, SM & Baber, C 2021, Distributed situation awareness for multi-agent mission in dynamic environments: a case study of multi-UAVs wildfires searching. in The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21). Proceedings of the AAAI Conference on Artificial Intelligence, no. 18, vol. 35, Association for the Advancement of Artificial Intelligence, pp. 15740-15741, 35th AAAI Conference on Artificial Intelligence, 2/02/21. <https://ojs.aaai.org/index.php/AAAI/article/view/17867>

Distributed situation awareness for multi-agent mission in dynamic environments: a case study of multi-UAVs wildfires searching. / Yusuf, Sagir Muhammad; Baber, Chris.
The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21). Association for the Advancement of Artificial Intelligence, 2021. p. 15740-15741 (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 35, No. 18).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - This thesis focuses attention on achieving Distributed Situation Awareness (DSA) with minimal resources (energy, processing cost, etc.) using small low-capacity agents (e.g., UAVs) coordinated in a decentralised fashion while conducting searching activity. This is in contrast to the existing works involving convoluted communication and information processing.

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KW - Optimisation

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Yusuf SM, Baber C. Distributed situation awareness for multi-agent mission in dynamic environments: a case study of multi-UAVs wildfires searching. In The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21). Association for the Advancement of Artificial Intelligence. 2021. p. 15740-15741. (Proceedings of the AAAI Conference on Artificial Intelligence; 18).

Distributed situation awareness for multi-agent mission in dynamic environments: a case study of multi-UAVs wildfires searching

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this