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Self-Evolving AI Agents for Industrial Process Optimization

Supervisor:

Professor Jianxin (Kevin) Li

Abstract

The proposed PhD research aims to develop trustworthy self-evolving AI agents for industrial process optimization in dynamic, data-rich and uncertain environments. The project will integrate large language models, multi-agent collaboration, industrial knowledge graphs, process mining, simulation-based optimization and human-in-the-loop feedback to enable agents to analyse process data, identify bottlenecks, diagnose causes, test alternative strategies and recommend optimization actions. Its significance lies in moving beyond static industrial AI systems toward adaptive, explainable and continuously improving decision-support. The research has strong potential impact in advanced manufacturing, energy, logistics and Industry 5.0 by improving efficiency, safety, sustainability and industrial innovation.

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