2019/03/06 by Shumpei Kubosawa, Kubosawa, Shumpei, Takashi Onishi +3
Computer Science · Engineering · Mathematics · #Advanced Data Processing Techniques #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Fault Detection and Control Systems #Fuzzy Logic and Control Systems #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #cs.AI #stat.ML
paper · pdf · doi:10.48550/arxiv.1903.02183
Proceedings of the SICE Annual Conference 2018 (pp.1376-1379)
arxiv created 2019/03/06 · openalex publication_date 2019/03/06 · arxiv updated 2019/03/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, disturbance, and operation procedures. For the purpose of supporting human operators of chemical plants, we are developing an AI system that can semi-automatically synthesize operation procedures for efficient and stable operation. Our system can provide not only appropriate operation procedures but also reasons why the procedures are considered to be valid. This is achieved by integrating automated reasoning and deep reinforcement learning technologies with a chemical plant simulator and external knowledge. Our preliminary experimental results demonstrate that it can synthesize a procedure that achieves a much faster recovery from a malfunction compared to standard PID control.