Why Human-Autonomy Teaming?

Shively, Robert J.; Lachter, Joel; Brandt, Summer L.; Matessa, Michael; Battiste, Vernol; Johnson, Walter W.

doi:10.1007/978-3-319-60642-2_1

Cited by 42 publications

(32 citation statements)

References 14 publications

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“…Increasing complexity of automation should therefore go together with a paradigm shift toward human-autonomy teaming based on a shared understanding of the situation. This includes bi-directional communication whenever a significant divergence in the understanding of a situation occurs to provide information missing for shared awareness of the human autonomy team (Shively et al, 2017). Anticipation of divergences and understanding human information needs to ensure shared awareness remains a challenge for human autonomy teaming (McNeese et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Tracing Pilots’ Situation Assessment by Neuroadaptive Cognitive Modeling

Klaproth

Vernaleken

Krol³

et al. 2020

Front. Neurosci.

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This study presents the integration of a passive brain-computer interface (pBCI) and cognitive modeling as a method to trace pilots' perception and processing of auditory alerts and messages during operations. Missing alerts on the flight deck can result in out-of-the-loop problems that can lead to accidents. By tracing pilots' perception and responses to alerts, cognitive assistance can be provided based on individual needs to ensure they maintain adequate situation awareness. Data from 24 participating aircrew in a simulated flight study that included multiple alerts and air traffic control messages in single pilot setup are presented. A classifier was trained to identify pilots' neurophysiological reactions to alerts and messages from participants' electroencephalogram (EEG). A neuroadaptive ACT-R model using EEG data was compared to a conventional normative model regarding accuracy in representing individual pilots. Results show that passive BCI can distinguish between alerts that are processed by the pilot as task-relevant or irrelevant in the cockpit based on the recorded EEG. The neuroadaptive model's integration of this data resulted in significantly higher performance of 87% overall accuracy in representing individual pilots' responses to alerts and messages compared to 72% accuracy of a normative model that did not consider EEG data. We conclude that neuroadaptive technology allows for implicit measurement and tracing of pilots' perception and processing of alerts on the flight deck. Careful handling of uncertainties inherent to passive BCI and cognitive modeling shows how the representation of pilot cognitive states can be improved iteratively for providing assistance.

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Section: Discussionmentioning

confidence: 99%

Tracing Pilots’ Situation Assessment by Neuroadaptive Cognitive Modeling

Klaproth

Vernaleken

Krol³

et al. 2020

Front. Neurosci.

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“…Autonomous machine behaviors will also appear and evolve in human work and living environments where they interact with humans. Progress in Artificial Intelligence, sensing and network technology, and cloud computing has led to the development of machine intelligence (ePartners) enabling machine systems to behave and communicate as team members or partners of humans [39].…”

Section: Interaction With and Understanding Of Humansmentioning

confidence: 99%

Telling autonomous systems what to do

Werkhoven

Kester

Neerincx

2018

Proceedings of the 36th European Conference on Cognitive Ergonomics

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Recent progress in Artificial Intelligence, sensing and network technology, robotics, and (cloud) computing has enabled the development of intelligent autonomous machine systems. Telling such autonomous systems "what to do" in a responsible way, is a non-trivial task. For intelligent autonomous machines to function in human society and collaborate with humans, we see three challenges ahead affecting meaningful control of autonomous systems. First, autonomous machines are not yet capable of handling failures and unexpected situations. Providing procedures for all possible failures and situations is unfeasible because the state-action space would explode. Machines should therefore become self-aware (self-assessment, self-management) enabling them to handle unexpected situations when they arise. This is a challenge for the computer science community. Second, in order to keep (meaningful) control, humans come into a new role of providing intelligent autonomous machines with objectives or goal functions (including rules, norms, constraints and moral values), specifying the utility of every possible outcome of actions of autonomous machines. Third, in order to be able to collaborate with humans, autonomous systems will require an understanding of (us) humans (i.e., our social, cognitive, affective and physical behaviors) and the ability to engage in partnership interactions (such as explanations of task performances, and the establishment of joint goals and work agreements). These are new challenges for the cognitive ergonomics community. CCS CONCEPTS • Human-centered computing → Interaction design → Interaction design theory, concepts and paradigms;

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“…For SPO, they argue that these requirements include the ability for automation to process natural language, intuit when to interrupt the pilot based on context, perform independent monitoring of aircraft state, provide verbal and visual indicators about when it is performing tasks or is thinking, take over for the pilot when needed, engage in self-diagnosis, and fail gracefully. Shively et al (2017) highlighted the notion of human-autonomy teaming (HAT) in the context of reduced-crew operations, where automation and human operators work together to solve problems. HAT represents a significant shift from the view that automation is a simple replacement for human functions to a view where the automation acts as an agent, and serves as a team member with the pilot.…”

Section: Cockpit-based Operational Conceptsmentioning

confidence: 99%

“…Shively et al (2017) indicated that the following qualities of the agent must be met to overcome problems associated with automation in reduced-crew operations. First, the human operator must understand the intent and reasoning of the agent and determine the factors used by the agent in arriving at a solution or recommendation.…”

Section: Cockpit-based Operational Conceptsmentioning

confidence: 99%

“…The captain could request information from either the dispatcher or the ACFP in all conditions. In half of the scenarios, the ACFP was enhanced based on tenets of good HAT (Shively et al, 2017), resulting in a more transparent and interactive interface with the automation (see Table 1). Key features of the enhanced ACFP included:…”

Section: Cockpit-based Operational Conceptsmentioning

confidence: 99%

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