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A Robust Decision-Making Framework Based on Collaborative Agents

Johana M. Florez-Lozano Orcid Logo, Fabio Caraffini Orcid Logo, Carlos Parra, Mario Gongora Orcid Logo

IEEE Access, Volume: 8, Pages: 150974 - 150988

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.1109/access.2020.3016784

Abstract

Making decisions under uncertainty is very challenging but necessary as most real-world scenarios are plagued by disturbances that can be generated internally, by the hardware itself, or externally, by the environment. Hence, we propose a general decision-making framework which can be adapted to opt...

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Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa60953
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last_indexed 2023-01-13T19:21:27Z
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spelling 2022-09-21T14:51:47.6731541 v2 60953 2022-08-28 A Robust Decision-Making Framework Based on Collaborative Agents d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2022-08-28 SCS Making decisions under uncertainty is very challenging but necessary as most real-world scenarios are plagued by disturbances that can be generated internally, by the hardware itself, or externally, by the environment. Hence, we propose a general decision-making framework which can be adapted to optimally address the most heterogeneous real-world domains without being significantly affected by undesired disturbances. Our paper presents a multi-agent based structure in which agents are capable of individual decision-making but also interact to perform subsequent, and more robust, collaborative decision-making processes. The complexity of each software agent can be kept quite low without a deterioration of the performance since an intelligent and robust-to-uncertainty decision-making behaviour arises when their locally produced measure of support are shared and exploited collaboratively. We show that by equipping agents with classic computational intelligence techniques, to extract features and generate measure of supports, complex hybrid multi-agent software structures capable of handling uncertainty can be easily designed. The resulting multi-agent systems generated with this approach are based on a two-phases decision-making methodology which first runs parallel local decision making processes to then aggregate the corresponding outputs to improve upon the accuracy of the system. To highlight the potential of this approach, we provided multiple implementations of the general framework and compared them over four different application scenarios. Results are promising and show that having a second collaborative decision-making process is always beneficial. Journal Article IEEE Access 8 150974 150988 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 27 8 2020 2020-08-27 10.1109/access.2020.3016784 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2022-09-21T14:51:47.6731541 2022-08-28T20:40:41.3117059 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Johana M. Florez-Lozano 0000-0002-3621-492x 1 Fabio Caraffini 0000-0001-9199-7368 2 Carlos Parra 3 Mario Gongora 0000-0002-7135-2092 4 60953__25186__e0d2a76d455e4b319e9a91cee526b098.pdf 60953_VoR.pdf 2022-09-21T14:51:00.7561707 Output 1925325 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution 4.0 License true eng https://creativecommons.org/licenses/by/4.0/
title A Robust Decision-Making Framework Based on Collaborative Agents
spellingShingle A Robust Decision-Making Framework Based on Collaborative Agents
Fabio Caraffini
title_short A Robust Decision-Making Framework Based on Collaborative Agents
title_full A Robust Decision-Making Framework Based on Collaborative Agents
title_fullStr A Robust Decision-Making Framework Based on Collaborative Agents
title_full_unstemmed A Robust Decision-Making Framework Based on Collaborative Agents
title_sort A Robust Decision-Making Framework Based on Collaborative Agents
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Johana M. Florez-Lozano
Fabio Caraffini
Carlos Parra
Mario Gongora
format Journal article
container_title IEEE Access
container_volume 8
container_start_page 150974
publishDate 2020
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/access.2020.3016784
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Making decisions under uncertainty is very challenging but necessary as most real-world scenarios are plagued by disturbances that can be generated internally, by the hardware itself, or externally, by the environment. Hence, we propose a general decision-making framework which can be adapted to optimally address the most heterogeneous real-world domains without being significantly affected by undesired disturbances. Our paper presents a multi-agent based structure in which agents are capable of individual decision-making but also interact to perform subsequent, and more robust, collaborative decision-making processes. The complexity of each software agent can be kept quite low without a deterioration of the performance since an intelligent and robust-to-uncertainty decision-making behaviour arises when their locally produced measure of support are shared and exploited collaboratively. We show that by equipping agents with classic computational intelligence techniques, to extract features and generate measure of supports, complex hybrid multi-agent software structures capable of handling uncertainty can be easily designed. The resulting multi-agent systems generated with this approach are based on a two-phases decision-making methodology which first runs parallel local decision making processes to then aggregate the corresponding outputs to improve upon the accuracy of the system. To highlight the potential of this approach, we provided multiple implementations of the general framework and compared them over four different application scenarios. Results are promising and show that having a second collaborative decision-making process is always beneficial.
published_date 2020-08-27T04:19:29Z
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score 11.036706

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