Two timescale immunological learning for idiotypic behaviour mediation

短期学习可以被定义为在一个人的一生中发生的，而长期学习可以是知识积累的净进化结果。与单独使用其中任何一种方法相比，这些方法的集成提供了优势。我们的免疫系统通过使用基因库和其他进化和免疫机制，毫不费力地将两者结合起来。我们的目标是模仿这一点，得出有用的规则，以自主解决导航/控制问题。如果没有长期的学习，控制器最初缺乏规则有效性的知识，因此不知道应该使用哪些规则。长期孤立学习的缺点是，由于所涉及的时间尺度，它必须在模拟中离线进行。模拟器的使用可能意味着结果不能很好地转化为物理世界。因此，我们提供了来自真实短期实验的反馈来验证数据。我们建议通过设计一种机器人控制器来研究短期和长期学习之间的关系，该控制器既考虑了学习周期，又允许它们的输出相互馈送。选择的体系结构是通过人工基因库创建初始规则，并通过加速(模拟)进化来建立规则数据库，该数据库由执行器功能索引。将派生规则的子集传输到物理机器人，该物理机器人使用人工免疫系统的动态来不断地适应和重新选择它们，其中规则通过独特型网络互连。因此，该方法提供了一套启动规则、对其有用性的衡量、更新和改进《汇辑》的手段以及自适应规则选择机制。

Short-term learning can be defined as taking place over the lifetime of an individual, whereas long-term learning can be the net evolutionary result of knowledge accumulation. The integration of these approaches provides an advantage over the use of either one alone. Our immune system combines both effortlessly through the use of gene libraries and other evolutionary and immunological mechanisms. We aim to imitate this to derive useful rules to autonomously solve navigation / control problems. Without long-term learning, the controller initially lacks knowledge of the effectiveness of rules and therefore has no sense of which ones to use. The disadvantage of long-term learning in isolation is that it must be conducted offline in simulation, due to the time scales involved. The use of simulators can mean that results translate poorly to the physical world. Hence we provide feedback from real short-term experiments to validate the data. We propose here to investigate the relationship between short- and long -term learning by designing a robot controller that considers both learning cycles and allows their outputs to feed into each other. The chosen architecture is the creation of initial rules through an artificial gene library and their accelerated (simulated) evolution to establish a rule database, indexed by actuator function. A subset of the derived rules is transferred to a physical robot that continually adapts and re-selects them using the dynamics of an Artificial Immune System, with rules interconnected through an idiotypic network. The methodology thus provides a set of starting rules, a measure of their usefulness, a means for updating and thus improving the repertoire and a mechanism for adaptive rule selection.