کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
424786 685642 2016 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Principles and experimentations of self-organizing embedded agents allowing learning from demonstration in ambient robotics
ترجمه فارسی عنوان
اصول و تجربیات عوامل تعبیه شده خودسازمان دهی که امکان یادگیری از نمایش را در رباتیک محیط فراهم می کند
کلمات کلیدی
عوامل سازگار؛ سیستم چند منظوره تعاونی؛ رباتیک در محیط های محیطی؛ فراگیری ماشین؛ زمینه آگاهی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی


• Extreme Sensitive Robotic as a bottom-up approach to deal with complexity in ambient robotic.
• Self-adaptive multi-agent system for learning from demonstration in ambient robotic.
• Experiments show that tutorship learning with Context agent is promising.

Ambient systems are populated by many heterogeneous devices to provide adequate services to their users. The adaptation of an ambient system to the specific needs of its users is a challenging task. Because human–system interaction has to be as natural as possible, we propose an approach based on Learning from Demonstration (LfD). LfD is an interesting approach to generalize what has been observed during the demonstration to similar situations. However, using LfD in ambient systems needs adaptivity of the learning technique. We present ALEX, a multi-agent system able to dynamically learn and reuse contexts from demonstrations performed by a tutor. The results of the experiments performed on both a real and a virtual robot show interesting properties of our technology for ambient applications.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 64, November 2016, Pages 78–87
نویسندگان
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