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XPERIENCE -机器人从经验中学习自我引导

XPERIENCE - Robots Bootstrapped through Learning from Experience
课程网址: http://videolectures.net/cogsys2012_dillmann_bootstrapped/  
主讲教师: Rüdiger Dillmann
开课单位: 卡尔斯鲁厄理工学院
开课时间: 2012-03-14
课程语种: 英语
中文简介:
当前法律制定的研究,体现认知是建立在两个核心观点:1)物理交互和探索世界的本质上允许代理获得和扩展,由认知表征,2)表示这样的交互也比人类更好的适应指导行为的规则或控制逻辑。然而,探索和辨别学习是相对缓慢的过程。另一方面,人类能够迅速地创造新的概念,并利用他们的经验对意想不到的情况作出反应。“想象”和“内部模拟”,因此依赖于先验知识的生成机制被用来预测近期的未来,是增加认知发展的带宽和速度的关键。目前的人工认知系统在这方面是有限的,因为它们还没有有效地利用这种生成机制来扩展其认知属性。解决方案:Xperience项目将通过结构化引导来解决这个问题,这个想法来自于儿童语言习得研究。结构引导是一种构建生成模型的方法,利用现有经验来预测未探索的行动效果,并为学习新概念集中假设空间。这种发展的方法能够从很少的额外培训数据中快速概括和获取新的知识和技能。此外,由于共享的概念,结构化引导使激活代理能够有效地与彼此和人类进行通信。结构引导可以应用于认知发展的所有层次(如感觉运动、计划、沟通)。
课程简介: Current research in enactive, embodied cognition is built on two central ideas: 1) Physical interaction with and exploration of the world allows an agent to acquire and extend intrinsically grounded, cognitive representations and, 2) representations built from such interactions are much better adapted to guiding behaviour than human crafted rules or control logic. Exploration and discriminative learning, however are relatively slow processes. Humans, on the other hand, are able to rapidly create new concepts and react to unanticipated situations using their experience. “Imagining” and “internal simulation”, hence generative mechanisms which rely on prior knowledge are employed to predict the immediate future and are key in increasing bandwidth and speed of cognitive development. Current artificial cognitive systems are limited in this respect as they do not yet make efficient use of such generative mechanisms for the extension of their cognitive properties. ;Solution: The Xperience project will address this problem by structural bootstrapping, an idea taken from child language acquisition research. Structural bootstrapping is a method of building generative models, leveraging existing experience to predict unexplored action effects and to focus the hypothesis space for learning novel concepts. This developmental approach enables rapid generalization and acquisition of new knowledge and skills from little additional training data. Moreover, thanks to shared concepts, structural bootstrapping enables enactive agents to communicate effectively with each other and with humans. Structural bootstrapping can be employed at all levels of cognitive development (e.g. sensorimotor, planning, communication).
关 键 词: 物理交互; 探索和辨别学习; 人工认知系统; 结构化引导; 人工智能发展
课程来源: 视频讲座网
最后编审: 2019-10-17:cwx
阅读次数: 61