Conceptualizing Team Working Memory: Implications for Human-Automation Collaboration

Miyake, T.; Parker, Charles; Hall, A.; Boring, Ronald L.

doi:10.1177/1071181321651025

Cited by 1 publication

(1 citation statement)

References 19 publications

(21 reference statements)

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“…Lastly, within the cognitive realm, individuals who scored high on measures of working memory showed less trust in automation than their lower working memory counterparts [24]. As the number of automated and human agents increases within networks, and interactions between the two become more complex, Miyake et al [25] have called for investigations into team-working memory on performance, as a way to better understand collaborative capacity within team mental models.…”

Section: Trust In Automationmentioning

confidence: 99%

Technical Basis for Advanced Artificial Intelligence and Machine Learning Adoption in Nuclear Power Plants

Agarwal

Walker

Manjunatha

et al. 2022

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The research and development reported here is part of the Technology Enabled Risk-Informed Maintenance Strategy project sponsored by the U.S. Department of Energy's Light Water Reactor Sustainability program. The primary objective of the research presented in this report is to produce a technical basis for developing explainable and trustable artificial intelligence (AI) and machine learning (ML) technologies. The technical basis will lay the foundation for addressing the technical and regulatory adoption challenges of AI/ML technologies across plant assets and the nuclear industry at scale and to achieve seamless cost-effective automation without compromising plant safety and reliability.The technical basis ensuring wider adoption of AI/ML technologies presented in this report was developed by Idaho National Laboratory (INL), in collaboration with Public Service Enterprise Group (PSEG) Nuclear, LLC. To develop the initial technical basis, the circulating water system (CWS) at the PSEG-owned plant sites was selected as the identified plant asset. Specifically, the issue of waterbox fouling diagnosis in the CWS using different types of CWS data is presented to address the said challenge. The approach presented in this report is based on the closed-loop forward-backward process that tries to capture the advancements in data science addressing the explainability of AI/ML outcomes, user-centric interpretability of those outcomes, and how user interpretation can be used as feedback to further simplify the process. A prototype interface is developed to present a focused component-level display of the ML model outputs in a usable and digestible form.

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Section: Trust In Automationmentioning

confidence: 99%