A Causal Model-based Planner for the Reconfiguration of Continuous Processes

This paper presents a planning framework for discrete planning of human operations in highly automated industrial plants, where manual and automatic control coexist. Based solely on qualitative knowledge of the controlled continuous process represented through signed directed graphs, the planner exploits a greedy algorithm to determine the optimal action for the human operator upon changes in the process' operating conditions or reconfiguration of the control system due to faulty conditions. The planner results in a decision support tool that instructs the human operator on the best course of action. The planning framework and the resulting planner are demonstrated on a quadruple-tank system.