A dynamic measurement of agent autonomy in the layered adjustable autonomy model

In a dynamically interactive systems that contain a mix of humans' and software agents' intelligence, managing autonomy is a challenging task. Giving an agent a complete control over its autonomy is a risky practice while manually setting the agent's autonomy level is an inefficient a...

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Main Authors: Mostafa S.A., Ahmad M.S., Ahmad A., Annamalai M., Mustapha A.
Other Authors: 37036085800
Format: Article
Published: Springer Verlag 2023
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spelling my.uniten.dspace-219602023-05-16T10:46:18Z A dynamic measurement of agent autonomy in the layered adjustable autonomy model Mostafa S.A. Ahmad M.S. Ahmad A. Annamalai M. Mustapha A. 37036085800 56036880900 55390963300 36138644000 57200530694 In a dynamically interactive systems that contain a mix of humans' and software agents' intelligence, managing autonomy is a challenging task. Giving an agent a complete control over its autonomy is a risky practice while manually setting the agent's autonomy level is an inefficient approach. In this paper, we propose an autonomy measurement mechanism and its related formulae for the Layered Adjustable Autonomy (LAA) model. Our model provides a mechanism that optimizes autonomy distribution, consequently, enabling global control of the autonomous agents that guides or even withholds them whenever necessary. This is achieved by formulating intervention rules on the agents' decision-making capabilities through autonomy measurement criteria. Our aim is to create an autonomy model that is flexible and reliable. © Springer International Publishing Switzerland 2014. Final 2023-05-16T02:46:18Z 2023-05-16T02:46:18Z 2014 Article 10.1007/978-3-319-01787-7_3 2-s2.0-84958534584 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958534584&doi=10.1007%2f978-3-319-01787-7_3&partnerID=40&md5=21bad1427a78bb70dfc3b1352422fccd https://irepository.uniten.edu.my/handle/123456789/21960 513 25 35 Springer Verlag Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description In a dynamically interactive systems that contain a mix of humans' and software agents' intelligence, managing autonomy is a challenging task. Giving an agent a complete control over its autonomy is a risky practice while manually setting the agent's autonomy level is an inefficient approach. In this paper, we propose an autonomy measurement mechanism and its related formulae for the Layered Adjustable Autonomy (LAA) model. Our model provides a mechanism that optimizes autonomy distribution, consequently, enabling global control of the autonomous agents that guides or even withholds them whenever necessary. This is achieved by formulating intervention rules on the agents' decision-making capabilities through autonomy measurement criteria. Our aim is to create an autonomy model that is flexible and reliable. © Springer International Publishing Switzerland 2014.
author2 37036085800
author_facet 37036085800
Mostafa S.A.
Ahmad M.S.
Ahmad A.
Annamalai M.
Mustapha A.
format Article
author Mostafa S.A.
Ahmad M.S.
Ahmad A.
Annamalai M.
Mustapha A.
spellingShingle Mostafa S.A.
Ahmad M.S.
Ahmad A.
Annamalai M.
Mustapha A.
A dynamic measurement of agent autonomy in the layered adjustable autonomy model
author_sort Mostafa S.A.
title A dynamic measurement of agent autonomy in the layered adjustable autonomy model
title_short A dynamic measurement of agent autonomy in the layered adjustable autonomy model
title_full A dynamic measurement of agent autonomy in the layered adjustable autonomy model
title_fullStr A dynamic measurement of agent autonomy in the layered adjustable autonomy model
title_full_unstemmed A dynamic measurement of agent autonomy in the layered adjustable autonomy model
title_sort dynamic measurement of agent autonomy in the layered adjustable autonomy model
publisher Springer Verlag
publishDate 2023
_version_ 1806425923711401984
score 13.214268