Task Allocation: Contemporary Methods for Assigning Human–Robot Roles

Kousi, Niki; Dimosthenopoulos, Dimosthenis; Aivaliotis, Sotiris; Michalos, George; Makris, Sotiris

doi:10.1007/978-3-030-78513-0_12

Cited by 5 publications

(3 citation statements)

References 78 publications

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“…There is a great need for humans to supervise and intervene in automated systems despite technological advancements. Therefore, future HMIs will require efficient coordination, and emphasize the importance of human trust in automation to achieve this coordination [9][10][11][12][13].…”

Section: A Background:hcis and Htbfcmentioning

confidence: 99%

Info-Geometric Analysis of Human-Trust Based Feedback Control (HTBFC) Five-Dimensional Manifold with HTBFC Applications to Robotics

A Mageed

2024

Preprint

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Using the potent tools of information geometry (IG), the current research analyses the information geometrics of the five-dimensional HTBFC Manifold. By enabling a unique revolutionary analysis for the performance of Human-Trust Based Feedback Control (HTBFC), the integration of information geometry into HTBFC facilitates the relativistic analysis of the five-dimensional HTBFC manifold. Both the two- and three-dimensional manifold subcases are investigated. This work provides a ground-breaking approach to IG unification of Human- Machine Interactions (HMIs). Some potential applications of HTBFC to robotics are presented.Conclusions are provided, along with some emerging open problems and recommendations for future research.

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Section: A Background:hcis and Htbfcmentioning

confidence: 99%

Info-Geometric Analysis of Human-Trust Based Feedback Control (HTBFC) Five-Dimensional Manifold with HTBFC Applications to Robotics

A Mageed

2024

Preprint

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“…However, optimal function allocation are not the primary focus in these applications. In human-robot collaboration studies, various optimization frameworks aim to maximize production performance in manufacturing [24], [25], [26]. Nevertheless, most of the existing work concentrates on interactions in the same physical location.…”

Section: A Related Work 1) Computational Modelingmentioning

confidence: 99%

A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario

Byeon,

Choi,

Hwang

2024

IEEE Open J. Control. Syst.

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This paper 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 humansubject 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.

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Section: A Background: Hcis and Htbfcmentioning

confidence: 99%

Human-Trust Based Feedback Control (HTBFC) Five-Dimensional Manifold Info-Geometric Analysis and how HTBEC Impacts Robotics

2024

Adv Mach Lear Art Inte

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This work examines the five-dimensional HTBFC Manifold’s info-geometrics using effective information geometry (IG) techniques. The performance of HTBFC can be analysed from a novel relativistic approach by merging IG framework. Subcases of manifolds in two and three dimensions are studied. Human-machine interactions (HMIs) can now be unified through IG in a novel way because to this study. The presentation includes the essential robotic applications of HTBFC. A few newly identified unsolved issues and suggestions for additional study are included in the conclusions.

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Task Allocation: Contemporary Methods for Assigning Human–Robot Roles

Cited by 5 publications

References 78 publications

Info-Geometric Analysis of Human-Trust Based Feedback Control (HTBFC) Five-Dimensional Manifold with HTBFC Applications to Robotics

Info-Geometric Analysis of Human-Trust Based Feedback Control (HTBFC) Five-Dimensional Manifold with HTBFC Applications to Robotics

A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario

Human-Trust Based Feedback Control (HTBFC) Five-Dimensional Manifold Info-Geometric Analysis and how HTBEC Impacts Robotics

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