کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946665 | 1439411 | 2017 | 29 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Understanding human intention by connecting perception and action learning in artificial agents
ترجمه فارسی عنوان
درک قصد انسانی از طریق ادراک و یادگیری عملی در عوامل مصنوعی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
To develop an advanced human-robot interaction system, it is important to first understand how human beings learn to perceive, think, and act in an ever-changing world. In this paper, we propose an intention understanding system that uses an Object Augmented-Supervised Multiple Timescale Recurrent Neural Network (OA-SMTRNN) and demonstrate the effects of perception-action connected learning in an artificial agent, which is inspired by psychological and neurological phenomena in humans. We believe that action and perception are not isolated processes in human mental development, and argue that these psychological and neurological interactions can be replicated in a human-machine scenario. The proposed OA-SMTRNN consists of perception and action modules and their connection, which are constructed of supervised multiple timescale recurrent neural networks and the deep auto-encoder, respectively, and connects their perception and action for understanding human intention. Our experimental results show the effects of perception-action connected learning, and demonstrate that robots can understand human intention with OA-SMTRNN through perception-action connected learning.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 92, August 2017, Pages 29-38
Journal: Neural Networks - Volume 92, August 2017, Pages 29-38
نویسندگان
Sangwook Kim, Zhibin Yu, Minho Lee,