Human–Autonomy Teaming: Definitions, Debates, and Directions

Lyons, Joseph B.; Sycara, Katia; Lewis, Michael; Capiola, August

doi:10.3389/fpsyg.2021.589585

Cited by 84 publications

(50 citation statements)

References 87 publications

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“…Algorithmic transparency without sufficient openness about stakeholder decisions, interests, and overall context, provides not much more than a peephole into a limited part of the whole socio-technical system. Contrary to popular belief, providing transparency-or even explanations-from the system does not mean that we can effectively contest the decisions (Aler Tubella et al, 2020;Lyons et al, 2021). Contestability, i.e., the ability to contest decisions, requires looking beyond why a decision was made.…”

Section: Transparency As Contestabilitymentioning

confidence: 95%

Let Me Take Over: Variable Autonomy for Meaningful Human Control

Methnani

Tubella

Dignum

et al. 2021

Front. Artif. Intell.

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As Artificial Intelligence (AI) continues to expand its reach, the demand for human control and the development of AI systems that adhere to our legal, ethical, and social values also grows. Many (international and national) institutions have taken steps in this direction and published guidelines for the development and deployment of responsible AI systems. These guidelines, however, rely heavily on high-level statements that provide no clear criteria for system assessment, making the effective control over systems a challenge. “Human oversight” is one of the requirements being put forward as a means to support human autonomy and agency. In this paper, we argue that human presence alone does not meet this requirement and that such a misconception may limit the use of automation where it can otherwise provide so much benefit across industries. We therefore propose the development of systems with variable autonomy—dynamically adjustable levels of autonomy—as a means of ensuring meaningful human control over an artefact by satisfying all three core values commonly advocated in ethical guidelines: accountability, responsibility, and transparency.

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Section: Transparency As Contestabilitymentioning

confidence: 95%

Let Me Take Over: Variable Autonomy for Meaningful Human Control

Methnani

Tubella

Dignum

et al. 2021

Front. Artif. Intell.

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“…In Figure 4 the aggregated population trust optimum x from ( 8) is compared with the Chebyshev centers for the preference polytopes belonging to a subset of participants. 2 For the same subset of participants, we also compare their distinctiveness z k 1 with corresponding TPS-HRI trust scores in Figure 6. We observe that participants with distinctiveness z k 1 ≤ 0.035 and a trust score in the range [42%, 56.5%] express preferences compatible with the population's preferences.…”

Section: B Resultsmentioning

confidence: 99%

“…Much attention has focused on trust in human-automation interaction, initially in human interactions with industrial machines [1] but later expanding to other settings [2]. Trust in human-robot interaction is an active area of research [3], stemming from scenarios that require a delegation of autonomy (e.g.…”

Section: Introductionmentioning

confidence: 99%

Individual and Team Trust Preferences for Robotic Swarm Behaviors

Vella¹,

Williams²,

Chapman³

et al. 2022

Preprint

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Trust between humans and multi-agent robotic swarms may be analyzed using human preferences. These preferences are expressed by an individual as a sequence of ordered comparisons between pairs of swarm behaviors. An individual's preference graph can be formed from this sequence. In addition, swarm behaviors may be mapped to a feature vector space. We formulate a linear optimization problem to locate a trusted behavior in the feature space. Extending to human teams, we define a novel distinctiveness metric using a sparse optimization formulation to cluster similar individuals from a collection of individuals' labeled pairwise preferences. The case of anonymized unlabeled pairwise preferences is also examined to find the average trusted behavior and minimum covariance bound, providing insights into group cohesion. A user study was conducted, with results suggesting that individuals with similar trust profiles can be clustered to facilitate human-swarm teaming.

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“…The robot, seen as an equal peer complying with the team's rules, should also be capable of varying its level of autonomy and involvement depending on environment and situation. Recently, research has been using the term Human-Autonomy Teaming (HATs) to describe humans and intelligent, autonomous agents working interdependently toward a common goal (O'Neill et al, 2020;Lyons et al, 2021). Human communication is naturally multimodal, with voice, facial expression and gestures considered as key channels supporting its realization.…”

Section: Face Recognitionmentioning

confidence: 99%

Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments

2022

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Industrial contexts, typically characterized by highly unstructured environments, where task sequences are difficult to hard-code and unforeseen events occur daily (e.g., oil and gas, energy generation, aeronautics) cannot completely rely upon automation to substitute the human dexterity and judgment skills. Robots operating in these conditions have the common requirement of being able to deploy appropriate behaviours in highly dynamic and unpredictable environments, while aiming to achieve a more natural human-robot interaction and a broad range of acceptability in providing useful and efficient services. The goal of this paper is to introduce a deliberative framework able to acquire, reuse and instantiate a collection of behaviours that promote an extension of the autonomy periods of mobile robotic platforms, with a focus on maintenance, repairing and overhaul applications. Behavior trees are employed to design the robotic system’s high-level deliberative intelligence, which integrates: social behaviors, aiming to capture the human’s emotional state and intention; the ability to either perform or support various process tasks; seamless planning and execution of human-robot shared work plans. In particular, the modularity, reactiveness and deliberation capacity that characterize the behaviour tree formalism are leveraged to interpret the human’s health and cognitive load for supporting her/him, and to complete a shared mission by collaboration or complete take-over. By enabling mobile robotic platforms to take-over risky jobs which the human cannot, should not or do not want to perform the proposed framework bears high potential to significantly improve the safety, productivity and efficiency in harsh working environments.

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Human–Autonomy Teaming: Definitions, Debates, and Directions

Cited by 84 publications

References 87 publications

Let Me Take Over: Variable Autonomy for Meaningful Human Control

Let Me Take Over: Variable Autonomy for Meaningful Human Control

Individual and Team Trust Preferences for Robotic Swarm Behaviors

Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments

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