Integrated learning systems that anticipate

预测的集成学习系统

• 批准号: 249885-2012
• 负责人: Trappenberg, Thomas
• 金额: $ 1.02万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=594355
• 关键词: Integrated; learning; systems; anticipate

Machines are often inflexible since they can not learn. My research goal is to understand and to build learning systems. Learning systems that we want to understand better includes biological organisms, in particular humans, where learning is an essential part of cognitive functions, and also machines, where learning can provide flexible solutions to new problems. The more specific research goal outlined in this proposal is to build learning system that can better guide decisions in complex environments. The system will integrate three major factors of learning, learning about sequences instead of static information more common in current systems, learning hierarchical representations of the environment to provide an efficient way of using such information, and combine such hierarchical temporal memories with learning that is guided by reward. The system will be applied to autonomous robots with the aim to search, identify and map objects in unknown territories. An example application is the identification of artificial objects under water from sonar data gathered by autonomous underwater vehicles (AUVs).

机器通常不灵活，因为它们无法学习。我的研究目标是理解和构建学习系统。我们想要更好地理解的学习系统包括生物有机体，特别是人类，其中学习是认知功能的重要组成部分，也包括机器，其中学习可以为新问题提供灵活的解决方案。该提案中概述的更具体的研究目标是建立能够更好地指导复杂环境中决策的学习系统。该系统将整合学习的三个主要因素，学习序列而不是当前系统中更常见的静态信息，学习环境的分层表示以提供使用此类信息的有效方式，并将这种分层时间记忆与奖励引导的学习相结合。该系统将应用于自主机器人，旨在搜索、识别和绘制未知区域中的物体。一个示例应用是根据自主水下航行器 (AUV) 收集的声纳数据识别水下人造物体。