Communications of the ACM
Decentralized Autonomous FMS Control by Hypothetical Reasoning Including Discrete Simulator
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Control of AGVs in decentralized autonomous FMS based on a mind model
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
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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.