Differences Between Experts and Novices in Decision-making Processes

被引:0
作者
Santos, Eugene, Jr. [1 ]
Kim, Keum Joo [1 ]
Nguyen, Hien [2 ]
机构
[1] Dartmouth Coll, Thayer Engn Sch, Hanover, NH 03755 USA
[2] UW Whitewater, Dept Comp Sci, Whitewater, WI 53190 USA
来源
UNMANNED SYSTEMS TECHNOLOGY XXII | 2020年 / 11425卷
关键词
decision-making process; Deep Neural Network; Double Transition Model; Inverse Reinforcement Learning; unmanned aerial vehicle; learning rate; reward distribution; computational model;
D O I
10.1117/12.2557957
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Experts and novices differ with respect to the use of intuition and deliberation in their decision-making processes, which affects the quality of decisions they make. We often ask ourselves: where does expert intuition and deliberation come from? If the progression from novice to expert is made through learning experiences, what should we provide novices during training? Identifying the discrepancies between experts and novices is essential for developing a computational learning system that simulates the human decision-making process. In this paper, we investigate the difference between individuals operating Unmanned Aerial Vehicle (UAV) missions, collected in a dataset called the Supervisory Control Operations User Test bed (SCOUT), by analyzing their computational models. For the computational models, deep neural networks (DNNs) and double transition models (DTMs) were employed. A set of DNNs was constructed from biometric information about eye movements, and a set of DTMs was built from event-driven data associated with actions taken by the individuals. For investigating DNNs, we examined how much improvement was obtained during training and validation, while for DTMs, we concentrated on the reward distributions of trajectories derived through inverse reinforcement learning (IRL). We classified SCOUT subjects into three levels of expertise according to their self-assessment and the maximum score achieved: novice, intermediate and expert. By analyzing these models, we identified differences between the expert and novice groups. In particular, the accuracy of the expert DNNs improved more effectively than that of novice DNNs, and the reward distributions of the expert DTMs were more closely clustered than those of novice DTMs.
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页数:9
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