通过社会互动学习的自传体记忆的连续发展水平

G. Pointeau, Maxime Petit, Peter Ford Dominey
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引用次数: 33

摘要

理想情况下,一个发展中的认知系统将获得与世界互动的知识,并将能够利用这些知识为逐步结构化的行为水平构建脚手架。目前的研究实现并测试了一种自传式记忆系统,通过该系统,人形机器人iCub可以积累与人类互动的经验,并提取表征这种经验的规律。然后,这些知识被用来形成共同经验的复合表征。我们首先将其应用于空间位置知识的发展,以及空间中物体之间的关系。然后,我们将演示如何将其扩展到事件之间的时间关系,包括“之前”和“之后”,它们在时间上构造事件的发生。在这个系统中,在与人进行长时间的互动之后,由此积累的经验以一种离线的方式被处理,以一种巩固的形式,在此期间,不同经验的共同元素被概括,以产生新的意义。这些学习到的意义形成了简单行为的基础,当这些行为被编码到自传式记忆中,就可以形成与人类共享经历的记忆基础,然后可以作为一种游戏或共享计划执行的形式被重复使用。
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Successive Developmental Levels of Autobiographical Memory for Learning Through Social Interaction
A developing cognitive system will ideally acquire knowledge of its interaction in the world, and will be able to use that knowledge to construct a scaffolding for progressively structured levels of behavior. The current research implements and tests an autobiographical memory system by which a humanoid robot, the iCub, can accumulate its experience in interacting with humans, and extract regularities that characterize this experience. This knowledge is then used in order to form composite representations of common experiences. We first apply this to the development of knowledge of spatial locations, and relations between objects in space. We then demonstrate how this can be extended to temporal relations between events, including “before” and “after,” which structure the occurrence of events in time. In the system, after extended sessions of interaction with a human, the resulting accumulated experience is processed in an offline manner, in a form of consolidation, during which common elements of different experiences are generalized in order to generate new meanings. These learned meanings then form the basis for simple behaviors that, when encoded in the autobiographical memory, can form the basis for memories of shared experiences with the human, and which can then be reused as a form of game playing or shared plan execution.
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来源期刊
IEEE Transactions on Autonomous Mental Development
IEEE Transactions on Autonomous Mental Development COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ROBOTICS
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审稿时长
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