TY - JOUR
T1 - Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms
T2 - A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies
AU - Abbas, Ammar N.
AU - Amazu, Chidera W.
AU - Mietkiewicz, Joseph
AU - Briwa, Houda
AU - Perez, Andres Alonso
AU - Baldissone, Gabriele
AU - Demichela, Micaela
AU - Chasparis, Georgios C.
AU - Kelleher, John D.
AU - Leva, Maria Chiara
N1 - Publisher Copyright:
© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2024
Y1 - 2024
N2 - In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time which resulted in a 95.8% prediction accuracy using hidden Markov model. These predictions enable the development of more effective intervention strategies.
AB - In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time which resulted in a 95.8% prediction accuracy using hidden Markov model. These predictions enable the development of more effective intervention strategies.
KW - AI-based recommendation system
KW - deep reinforcement learning
KW - dynamic influence diagrams
KW - eye tracking
KW - hidden Markov models
KW - human-in-the-loop AI
KW - human-machine interaction
KW - Process safety
KW - situational awareness
KW - workload
UR - http://www.scopus.com/inward/record.url?scp=85203065482&partnerID=8YFLogxK
U2 - 10.1080/10447318.2024.2391605
DO - 10.1080/10447318.2024.2391605
M3 - Article
AN - SCOPUS:85203065482
SN - 1044-7318
JO - International Journal of Human-Computer Interaction
JF - International Journal of Human-Computer Interaction
ER -