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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/13837

タイトル: Long-term Knowledge Acquisition Using Contextual Information in a Memory-inspired Robot Architecture
著者: Pratama, Ferdian
Mastrogiovanni, Fulvio
Lee, Soon Geul
Chong, Nak Young
キーワード: robot cognitive architecture
developmental learning
long-term knowledge acquisition
context-based memory retrieval
発行日: 2016-02-03
出版者: Taylor & Francis
誌名: Journal of Experimental and Theoretical Artificial Intelligence
巻: 29
号: 2
開始ページ: 313
終了ページ: 334
DOI: 10.1080/0952813X.2015.1134679
抄録: In this paper, we present a novel cognitive framework allowing a robot to form memories of relevant traits of its perceptions and to recall them when necessary. The framework is based on two main principles: on the one hand, we propose an architecture inspired by current knowledge inhuman memory organisation; on the other hand, we integrate such an architecture with the notion of context, which is used to modulate the knowledge acquisition process when consolidatingmemories and forming new ones, as well as with the notion of familiarity, which is employed to retrieve propermemories given relevant cues. Although much research has been carried out, which exploits Machine Learning approaches to provide robots with internal models of their environment (including objects and occurring events therein), we argue that such approaches may not be theright direction to follow if a long-term, continuous knowledge acquisition is to be achieved. As a case study scenario, we focus on both robot-environment and human-robot interaction processes. In case of robot?environment interaction, a robot performs pick and place movements using the objects in theworkspace, at the same time observing their displacement on a table in front of it, and progressively forms memories defined as relevant cues (e.g. colour, shape or relative position) in a context-aware fashion. As far as human-robot interaction is concerned, the robot can recall specific snapshots representing past events using both sensory informationand contextual cues upon request by humans.
Rights: This is an Author's Accepted Manuscript of an article published in Journal of Experimental and Theoretical Artificial Intelligence, 29(2), 2016, 313-334. Copyright (C) 2016 Taylor & Francis, available online at: http://dx.doi.org/10.1080/0952813X.2015.1134679
URI: http://hdl.handle.net/10119/13837
資料タイプ: author
出現コレクション:b10-1. 雑誌掲載論文 (Journal Articles)


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