Studies on Adaptive and Learning Agents on Production System

点击翻译按钮获取中文摘要

In order to realize an agent system with adaptive and learning functions on a production system, it is practically requested to develop the agent system with a low level intelligence rather than a high level intelligence. I have developed the agent system with adaptive and learning function from a viewpoint of the high level intelligence for the last two years. However, a learning method I have adopted needs a large capacity of memory and highly computing speed. The agent system with the low level intelligence means that (1) the agents concern his/her local circumstances, (2) they communicate with other agents within the local circumstances, (3) they make their decision what to do, and (4) the whole system attains its purpose. To develop such an agent system, a self-organizing maps (SOM) concept is introduced. SOM consists of neurons and their synapses. Its neurons proceed to organize themselves toward the arranged system by only updating their local synapses. When we regard the neuron … More s and synapses as production agents and their associated information, respectively, it becomes possible to construct the agent system with the adaptable low-level intelligence. This concept is applied to a vehicle path-planning problem (VPM) and a vehicle delivery-planning problem (VDPP) in a virtual factory.On VDP and VDPP, vehicles are regarded as the agents. SOM requests some topology relation between the agents. On VDP a straight-line topology is adopted for this purpose. When a start and goal locations are given to the agents, many agents are located in the factory at random and they have the straight-line topology relation. It is recognized that SOM makes intersection paths around the start and goal point locations. Then, multi-knot concept, which is used in the B-spline interpolation method, is employed to overcome this defect. Numerical experiments verify that the agents only exchange their information and finally generates a near optimum vehicle path. As for VDPP, the agents are located in the factory at random and multiple loops are set as their topology among them. Several agents are located and fixed at the delivery center location on each loop just as a multi-knot. Numerical experiments verify that the agents construct nearly equal-length loops as the vehicle paths. These results are presented in JSME and JSPE annual conference in Japan. Less