Two timescale immunological learning for idiotypic behaviour mediation

独特型行为调节的两个时间尺度的免疫学学习

• 批准号: EP/E003257/1
• 负责人: Uwe Aickelin
• 金额: $ 23.6万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE003257%2F1
• 关键词: Two; timescale; immunological; learning; idiotypic

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.

短期学习可以被定义为发生在一个人的一生中，而长期学习可以是知识积累的净进化结果。这些方法的集成提供了优于单独使用任何一种方法的优势。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，以获得有用的规则，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，因此不知道使用哪些规则。长期孤立学习的缺点是，由于涉及的时间尺度，它必须在模拟中离线进行。使用模拟器可能意味着结果很难转化为现实世界。因此，我们提供反馈，从真实的短期实验，以验证数据。我们建议在这里调查短期和长期学习之间的关系，设计一个机器人控制器，考虑两个学习周期，并允许他们的输出到对方。所选择的体系结构是通过人工基因库和它们的加速（模拟）进化来建立规则数据库的初始规则的创建，由执行器函数索引。派生规则的子集被转移到物理机器人，该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，该方法提供了一套起始规则，其有用性的措施，一种手段，用于更新，从而提高了剧目和自适应规则选择的机制。

项目成果

Programming Mobile Robots with Aria and Player: A Guide to C++ Object-Oriented Control

使用 Aria 和 Player 对移动机器人进行编程：C 面向对象控制指南

• 发表时间: 2009
• 作者: A Whitbrook

The Use of Probabilistic Systems to Mimic the Behaviour of Idiotypic AIS Robot Controllers

使用概率系统来模拟独特的 AIS 机器人控制器的行为

• 期刊: Journal of Systemics, Cybernetics and Informatics
• 作者: Uwe Aickelin (Author)

Genetic-algorithm Seeding of Idiotypic Networks for Mobile-robot Navigation

用于移动机器人导航的独特型网络的遗传算法播种

• DOI: 10.2139/ssrn.2831226
• 发表时间: 2008
• 期刊: ArXiv
• 作者: Amanda M. Whitbrook;U. Aickelin;J. Garibaldi
• 通讯作者: J. Garibaldi

The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms

进化的人工免疫系统行为在小型和大型机器人平台之间的转移

• 发表时间: 2009
• 作者: A Whitbrook