Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies

• URL: http://arxiv.org/abs/2402.13219v1
• Date: Tue, 20 Feb 2024 18:31:27 GMT
• Title: Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies
• Authors: Ammar N. Abbas, Chidera W. Amazu, Joseph Mietkiewicz, Houda Briwa, Andres Alonzo Perez, Gabriele Baldissone, Micaela Demichela, Georgios G. Chasparis, John D. Kelleher, and Maria Chiara Leva
• Abstract summary: In complex industrial and chemical process control rooms, effective decision-making is crucial for safety andeffi- ciency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface.
• Score: 0.9378955659006951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and effi- ciency. 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 influ- ence 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 effec- tiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide valuable 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. These predictions enable the development of more effective intervention strategies.

Related papers

• Distribution-Aware Compensation Design for Sustainable Data Rights in Machine Learning [6.322978909154803]
  We propose an innovative mechanism that views this challenge through the lens of game theory. Our approach quantifies the ripple effects of data removal through a comprehensive analytical model. We establish mathematical foundations for measuring participant utility and system outcomes.
  arXiv  Detail & Related papers  (2024-10-19T09:04:13Z)
• Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming [1.3980986259786223]
  We propose a method to incorporate model uncertainty, thus enabling risk-sensitive sequential decision-making. Experiments were conducted with a group of twenty-six human participants within a simulated robot teleoperation environment.
  arXiv  Detail & Related papers  (2024-02-08T14:27:34Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Optimising Human-AI Collaboration by Learning Convincing Explanations [62.81395661556852]
  We propose a method for a collaborative system that remains safe by having a human making decisions. Ardent enables efficient and effective decision-making by adapting to individual preferences for explanations.
  arXiv  Detail & Related papers  (2023-11-13T16:00:16Z)
• Lessons Learned from EXMOS User Studies: A Technical Report Summarizing Key Takeaways from User Studies Conducted to Evaluate The EXMOS Platform [5.132827811038276]
  Two user studies aimed at illuminating the influence of different explanation types on three key dimensions: trust, understandability, and model improvement. Results show that global model-centric explanations alone are insufficient for effectively guiding users during the intricate process of data configuration. We present essential implications for developing interactive machine-learning systems driven by explanations.
  arXiv  Detail & Related papers  (2023-10-03T14:04:45Z)
• Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
  This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs) The system is into three inter-connected core processes-interaction, reflection, and reaction. Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
  arXiv  Detail & Related papers  (2023-09-15T02:42:03Z)
• Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection [57.13665112065285]
  Human-Object Interaction (HOI) detection is a challenging computer vision task. We present a framework that enhances HOI detection by incorporating structured text knowledge.
  arXiv  Detail & Related papers  (2023-07-25T14:20:52Z)
• An Interactive Explanatory AI System for Industrial Quality Control [0.8889304968879161]
  We aim to extend the defect detection task towards an interactive human-in-the-loop approach. We propose an approach for an interactive support system for classifications in an industrial quality control setting.
  arXiv  Detail & Related papers  (2022-03-17T09:04:46Z)
• Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
  We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
  arXiv  Detail & Related papers  (2022-03-14T17:40:42Z)
• Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study [6.076137037890219]
  We investigate how the interaction between a human and a continually learning prediction agent develops as the agent develops competency. We develop a virtual reality environment and a time-based prediction task wherein learned predictions from a reinforcement learning (RL) algorithm augment human predictions. Our findings suggest that human trust of the system may be influenced by early interactions with the agent, and that trust in turn affects strategic behaviour.
  arXiv  Detail & Related papers  (2021-12-14T22:46:44Z)
• End-to-End Models for the Analysis of System 1 and System 2 Interactions based on Eye-Tracking Data [99.00520068425759]
  We propose a computational method, within a modified visual version of the well-known Stroop test, for the identification of different tasks and potential conflicts events. A statistical analysis shows that the selected variables can characterize the variation of attentive load within different scenarios. We show that Machine Learning techniques allow to distinguish between different tasks with a good classification accuracy.
  arXiv  Detail & Related papers  (2020-02-03T17:46:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.