Human-Automation Collaboration in Dynamic Mission Planning: A Challenge Requiring an Ecological Approach

By Michael Linegang, H. Stoner, M. J. Patterson, B. Seppelt, J. Hoffman, Zachariah B. Crittendon, John D. Lee. 2006

95 citations recorded by the source corpus.

Subfields: Safety, Risk, Reliability and Quality, Social Psychology, Surgery

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Citations in the corpus

Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “This resurgence is driven by evidence that many AI applications have limited take up, or are not appropriated at all, due to ethical concerns [2] and a lack of trust on behalf of their users [165, 101].”
Intelligent Agent Transparency in Human–Agent Teaming for Multi-UxV Management (2016, background): “Past research has demonstrated that human operators sometimes question the accuracy and effectiveness of the output produced by IAs due to operator difficulty with understanding IA rationale, leading to reduced use of the IA and subsequent loss of performance (Linegang et al., 2006).”
Agent Reasoning Transparency’s Effect on Operator Workload (2016, background): “Workload is expected to increase as agent reasoning transparency (ART) increases (Linegang et. al., 2006; Lyons, 2013; Miller, 2014).”
Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues (2014, background): “the incorporation of implicit goals that agents may not be aware of [30], [78], [79]; 2) supervisory control in nature—to ensure”
Situation Awareness-Based Agent Transparency (2014, unknown): “, 2007) and the rationales behind the behaviors (Linegang et al., 2006).”
Situation awareness-based agent transparency and human-autonomy teaming effectiveness (2018, background): “…or disuse of the automated systems, it is crucial that an agent ’ s behaviours, reasoning, and expected outcomes be transparent to the operator (Bitan and Meyer 2007; de Visser et al. 2014; Linegang et al. 2006; Parasuraman and Riley 1997; Seppelt and Lee 2007; Stanton, Young, and Walker 2007).”
Transparency in artificial intelligence (2020, background): “The development of explainable AI is then, arguably, driven by evidence that many AI applications are not used in practice, partly due to users lacking trust in them (see Linegang et al., 2006).”
Increasing the Transparency of Unmanned Systems: Applications of Ecological Interface Design (2014, background): “…focused on physical process systems, such as nuclear power generation and petro-chemical refinement [9,10], the EID approach has been extended to settings as diverse as anesthesiology [11], military command and control [12], and the supervisory con-trol of unmanned vehicles and robot teams [13,14].”