An Idiotypic Immune Network as a Short-Term Learning Architecture for Mobile Robots
An Idiotypic Immune Network as a Short-Term Learning Architecture for Mobile Robots
复制标题
作为移动机器人短期学习架构的独特免疫网络
DOI：
10.1007/978-3-540-85072-4_24
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
J. Garibaldi
中科院分区：
文献类型：
--
作者：
Amanda M. Whitbrook;U. Aickelin;J. Garibaldi
提出了一种短期学习（STL）和长期学习（LTL）相结合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由使用遗传算法的快速模拟组成，以获得不同的行为集。然后将这些组转移到独特型人工免疫系统（AIS），形成STL阶段，该系统被称为播种。将LTL-STL组合方法与仅使用STL和使用手工设计的控制器进行了比较。此外，在关闭独特型机制时测试STL阶段。结果提供了大量证据，证明最佳选择是种子独特型系统，即将LTL与用于STL的独特型AIS合并的架构。他们还表明，结构上不同的环境可以用于这两个阶段，而不会影响可转移性。
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 hand-designed 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.