Learnable Evolutionary Optimization in Autonomous Pickup & Delivery Planning
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چکیده
This report describes an evolutionary approach to distributed planning by transport agents. These agents, representing trucks, autonomously decide on their transport orders by interacting with cargo agents representing containers. The agents use a guided evolutionary computation method, called the learnable evolution model, to create transport plans and render optimized decisions on which cargo to transport. The model is implemented within the PlaSMA multiagent simulation platform, and evaluated experimentally.
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تاریخ انتشار 2010