Framing Automation and Human Error in the Context of the Skill, Rule and Knowledge Taxonomy
M. M.René van Paassen (TU Delft - Control & Simulation)
A. Landman (TU Delft - Control & Simulation)
Clark Borst (TU Delft - Control & Simulation)
Max Mulder (TU Delft - Control & Simulation)
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Abstract
Automation errors may result in human performance issues that are often difficult to grasp. Skraaning and Jamieson (2023) proposed a taxonomy for classifying automation errors into categories based on the visible symptoms of design problems, so as to benefit the design of training scenarios. In this paper, we propose a complementary classification that is based on the mechanisms of human-automation interaction guided by Rasmussen’s Skill, Rule and Knowledge (SRK) taxonomy. We identified four main failure classes and expect that this classification can support automation designers.
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