Validating Human–Robot Interaction Schemes in Multitasking Environments

Crandall, Jacob W.; Goodrich, Michael A.; Olsen, Dan R.; Nielsen, Curtis W.

doi:10.1109/tsmca.2005.850587

Cited by 213 publications

(199 citation statements)

References 38 publications

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“…The key indicator in such systems is the fan-out of a Human-agents team as defined by Olsen and Wood in [37], [38] to be the number of agents that a Human can control simultaneously. The examples, found in literature, deal mainly with Human-multiple robot interaction/control [23], [39]. In this context the fan-out for a Human/robots team can reach 18 homogenous robots [40].…”

Section: B the Human/multi-agent System Interaction Issuementioning

confidence: 99%

Sphericall: A Human/Artificial Intelligence interaction experience

Gechter¹,

Ronzani²,

Rioli³

2014

IJIMAI

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-49- Abstract -Multi-agent systems are now wide spread in scientific works and in industrial applications. Few applications deal with the Human/Multi-agent system interaction. Multi-agent systems are characterized by individual entities, called agents, in interaction with each other and with their environment. Multiagent systems are generally classified into complex systems categories since the global emerging phenomenon cannot be predicted even if every component is well known. The systems developed in this paper are named reactive because they behave using simple interaction models. In the reactive approach, the issue of Human/system interaction is hard to cope with and is scarcely exposed in literature. This paper presents Sphericall, an application aimed at studying Human/Complex System interactions and based on two physics inspired multi-agent systems interacting together. The Sphericall device is composed of a tactile screen and a spherical world where agents evolve. This paper presents both the technical background of Sphericall project and a feedback taken from the demonstration performed during OFFF Festival in La Villette (Paris).

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Section: B the Human/multi-agent System Interaction Issuementioning

confidence: 99%

Sphericall: A Human/Artificial Intelligence interaction experience

Gechter¹,

Ronzani²,

Rioli³

2014

IJIMAI

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“…is the ConCo conformance curve; τ is a window of time over which the measure is taken ConCo 3.6 Neglect Tolerance: Neglect and Interaction Times Neglect tolerance refers to the frequency and duration of human-robot interactions required for the robot to maintain acceptable performance for a given system (Crandall, Goodrich, Olsen, & Nielsen, 2005). In its simplest form, neglect tolerance is modeled by neglect time (the average amount of time the robot can be ignored by the operator before its performance drops below some threshold) and interaction time (the average duration of a single human-robot interaction) .…”

Section: Name Category Definition Descriptionmentioning

confidence: 99%

Cognitive Telepresence in Human-Robot Interactions

Harutyunyan¹,

Manohar²,

Gezehei³

et al. 2013

Journal of Human-Robot Interaction

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Remote teleoperation of advanced, semi-autonomous robotic technologies has great potential in many industries critical to the economy and for the environment of the Middle East. Applications include maintenance of under-water oil wells, maintenance of nuclear power plants, counter-terrorism and national defense, law enforcement, remote sensing, and health care. In each of these applications, a user, likely without technology expertise, must operate a complex robot in uncertain and unknown environments. The nature of the tasks and environments encountered by the robot in these applications make it highly likely that the robot's limited autonomy will fail or be insufficient to complete the desired task. In this paper, we argue that, in such scenarios, cognitive telepresence, defined as the ability of the user to comprehend and control the robot's cognition, is an important design principle for human-robot systems. We compare and contrast cognitive telepresence to existing design principles commonly discussed in the literature, and define various metrics of cognitive telepresence. Finally, via two illustrative examples and a user study, we demonstrate the usefulness of cognitive telepresence as an important design principle of human-robot systems consisting of a user with limited technology expertise and a robot with limited and error-prone artificial intelligence.

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“…Crandall, Cummings, and Mitchell [7], [8] have introduced "fan-out" models to estimate the maximum number of robots a single operator can supervise in a given SHMR context. These estimates are based on neglect time (NT), the time a robot may be neglected before its performance falls below a predetermined threshold; interaction time (IT), the time a operator needs to interact with a robot to restore its performance to an above threshold level; wait time attention (WTA), the time required for the operator to notice that the robot requires maintenance; and wait time queue (WTQ), the delay in interacting with the robot when other robots require maintenance at the same time.…”

Section: B Cognitive Modelmentioning

confidence: 99%

High assurance human-centric decision systems

Heitmeyer¹,

Pickett

Breslow

et al. 2013

2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE)

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Abstract-Many future decision support systems will be human-centric, i.e., require substantial human oversight and control. Because these systems often provide critical services, high assurance will be needed that they satisfy their requirements. How to develop "high assurance human-centric decision systems" is unknown: while significant research has been conducted in areas such as agents, cognitive science, and formal methods, how to apply and integrate the design principles and disparate models in each area is unclear. This paper proposes a novel process for developing human-centric decision systems where AI (artificial intelligence) methods-namely, cognitive models to predict human behavior and agents to assist the humanare used to achieve adequate system performance, and software engineering methods, namely, formal modeling and analysis, to obtain high assurance. To support this process, the paper introduces a software engineering technique-formal model synthesis from scenarios-and two AI techniques-a model for predicting human overload and user model synthesis from participant studies data. To illustrate the process and techniques, the paper describes a decision system controlling unmanned air vehicles.

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Validating Human–Robot Interaction Schemes in Multitasking Environments

Cited by 213 publications

References 38 publications

Sphericall: A Human/Artificial Intelligence interaction experience

Sphericall: A Human/Artificial Intelligence interaction experience

Cognitive Telepresence in Human-Robot Interactions

High assurance human-centric decision systems

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