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 the author calls reasoning to anticipate the future (RAF), which controls automatic guided vehicles (AGVs) in autonomous decentralized 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 that was constructed on a computer. The results showed that, compared with conventional reasoning, this reasoning system reduced the number of hypothesis replacements until a true hypothesis was reached.
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