移动机器人短期学习的独特型免疫网络结构
An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots
摘要：
结合短期学习（STL）和长期学习（LTL）的方法来解决移动机器人导航问题，并在真实和模拟环境中进行了测试。LTL包括使用遗传算法的快速模拟，以获得不同的行为集。然后将这些集合转移到独特型人工免疫系统（AIS）中，形成STL阶段，并且该系统被称为种子。结合LTL-STL的方法相比，仅使用STL，并与使用一个手工设计的控制器。此外，当独特型机制关闭时，测试STL阶段。结果提供了大量的证据表明，最好的选择是种子独特型系统，即架构，合并LTL与独特型AIS的STL。他们还表明，结构上不同的环境可以用于两个阶段，而不损害可转移性
英文摘要：
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined LTL-STL approach is compared with using STL only, and with using a handdesigned controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferability
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
