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Real-time control of decentralised autonomous flexible manufacturing systems by using memory and oblivion

This paper describes a method that uses memory to determine a priority ranking for competing hypotheses. The aim is to increase the reasoning efficiency of a system which controls Automatic Guided Vehicles (AGVs) in Autonomous Decentralised Flexible Manufacturing Systems (AD-FMSs). The system includes memory data of past production conditions and AGV actions. Using these memory data, the system reorders hypotheses by giving the highest priority ranking to the hypothesis that is most likely to be true. The system was applied to an AD-FMS constructed on a computer. The results showed that this reasoning system reduced the number of hypothesis replacements.