A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario
This article proposes a quantitative framework for optimally allocating task functions in human-autonomy teaming (HAT). HAT involves cooperation between humans and autonomous agents to achieve common goals. As humans and autonomous agents possess different capabilities, function allocation plays a c...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Control Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10345767/ |
| _version_ | 1797376288027049984 |
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| author | Sooyung Byeon Joonwon Choi Inseok Hwang |
| author_facet | Sooyung Byeon Joonwon Choi Inseok Hwang |
| author_sort | Sooyung Byeon |
| collection | DOAJ |
| description | This article proposes a quantitative framework for optimally allocating task functions in human-autonomy teaming (HAT). HAT involves cooperation between humans and autonomous agents to achieve common goals. As humans and autonomous agents possess different capabilities, function allocation plays a crucial role in ensuring effective HAT. However, designing the best adaptive function allocation remains a challenge, as existing methods often rely on qualitative rules and intensive human-subject studies. To address this limitation, we propose a computational function allocation approach that leverages cognitive engineering, computational work models, and optimization techniques. The proposed optimal adaptive function allocation method is composed of three main elements: 1) analyze the teamwork to identify a set of all possible function allocations within a team construction, 2) numerically simulate the teamwork in temporal semantics to explore the interaction of the team with complex environments using the identified function allocations in a trial-and-error manner, and 3) optimize the adaptive function allocation with respect to a given situation such as physical conditions, available information resources, and human mental workload. For the optimization, we utilize performance metrics such as task performance, human mental workload, and coherency in function allocations. To illustrate the effectiveness of the proposed framework, we present a simulated HAT scenario involving a human work model and drone fleet for last-mile delivery in disaster relief operations. |
| first_indexed | 2024-03-08T19:36:23Z |
| format | Article |
| id | doaj.art-6b021834c98a469db232eedd643ce7cc |
| institution | Directory Open Access Journal |
| issn | 2694-085X |
| language | English |
| last_indexed | 2024-03-08T19:36:23Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Control Systems |
| spelling | doaj.art-6b021834c98a469db232eedd643ce7cc2023-12-26T00:12:51ZengIEEEIEEE Open Journal of Control Systems2694-085X2024-01-013324410.1109/OJCSYS.2023.334003410345767A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming ScenarioSooyung Byeon0https://orcid.org/0000-0003-4297-0331Joonwon Choi1https://orcid.org/0000-0001-8470-3528Inseok Hwang2https://orcid.org/0000-0001-7847-9865School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USASchool of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USASchool of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USAThis article proposes a quantitative framework for optimally allocating task functions in human-autonomy teaming (HAT). HAT involves cooperation between humans and autonomous agents to achieve common goals. As humans and autonomous agents possess different capabilities, function allocation plays a crucial role in ensuring effective HAT. However, designing the best adaptive function allocation remains a challenge, as existing methods often rely on qualitative rules and intensive human-subject studies. To address this limitation, we propose a computational function allocation approach that leverages cognitive engineering, computational work models, and optimization techniques. The proposed optimal adaptive function allocation method is composed of three main elements: 1) analyze the teamwork to identify a set of all possible function allocations within a team construction, 2) numerically simulate the teamwork in temporal semantics to explore the interaction of the team with complex environments using the identified function allocations in a trial-and-error manner, and 3) optimize the adaptive function allocation with respect to a given situation such as physical conditions, available information resources, and human mental workload. For the optimization, we utilize performance metrics such as task performance, human mental workload, and coherency in function allocations. To illustrate the effectiveness of the proposed framework, we present a simulated HAT scenario involving a human work model and drone fleet for last-mile delivery in disaster relief operations.https://ieeexplore.ieee.org/document/10345767/Computational work modelfunction allocationhuman-automation interactionhuman-autonomy teaminghuman-vehicle systems |
| spellingShingle | Sooyung Byeon Joonwon Choi Inseok Hwang A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario IEEE Open Journal of Control Systems Computational work model function allocation human-automation interaction human-autonomy teaming human-vehicle systems |
| title | A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario |
| title_full | A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario |
| title_fullStr | A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario |
| title_full_unstemmed | A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario |
| title_short | A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario |
| title_sort | computational framework for optimal adaptive function allocation in a human autonomy teaming scenario |
| topic | Computational work model function allocation human-automation interaction human-autonomy teaming human-vehicle systems |
| url | https://ieeexplore.ieee.org/document/10345767/ |
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