Integrated learning systems that anticipate

机器往往是不灵活的，因为它们不能学习。我的研究目标是理解和建立学习系统。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，还有机器，其中学习可以为新问题提供灵活的解决方案。本提案提出的更具体的研究目标是建立能够在复杂环境中更好地指导决策的学习系统。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这些信息的有效方法，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，目的是搜索、识别和绘制未知区域的物体。一个应用实例是利用自主水下航行器（auv）收集的声纳数据识别水下人工物体。

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).