Mining tacit knowledge of pilots in flight missions based on visual cognitive graph

Abstract

<div>As a highly dynamic operating process, flight activity requires a lot of attention from pilots. Thus, it’s quite imperative to give research to their visual attention. Traditional research methods mostly based on qualitative analysis, or hypothetical model, and seldom put context information into their model. However, the underlying knowledge (tacit knowledge) hidden in the different performances of pilot’s attention allocation is context related, and is hard to express by experts, thus it is difficult to use those traditional methods to construct an interaction system. In this paper, we mined attention pattern with scene context to achieve the quantitative analysis of tacit knowledge of pilots during flight tasks, and use the method of data mining as well as attribute graph model to construct visual cognitive graph(s). The attribute graph model was adopted to construct visual cognitive graphs which associate the obtained visual information within the flight context. Based on the model, the attention pattern with scene context was mined to achieve the quantitative analysis of tacit knowledge of pilots during flight tasks. Besides, three physical quantities derived from graph theory was introduced to describe the tacit knowledge, which can be used directly to construct an interaction system: first, key information, which shown as central node in the graph we built, reveals the most important information during flight mission within context; second, relevant information, which contains several nodes that was closely connected and strongly impact the central node, reveals the factors affecting the key information; third, bridge information based on betweenness centrality, which can be regard as the important information bridge(s), reveals the process of decision making. Our work can be directly used to train novice pilots, to guide the interface design, and to construct the adaptive interaction system.</div>