A semantic approach for enhancing assistive services in ubiquitous robotics

被引:10
|
作者
Ayari, Naouel [1 ]
Chibani, Abdelghani [1 ]
Amirat, Yacine [1 ]
Matson, Eric [2 ,3 ]
机构
[1] UPEC, LISSI Lab, Creteil, France
[2] Purdue Univ, Lab M2M, Rice Ctr, W Lafayette, IN 47907 USA
[3] Dongguk Univ, Dept Comp Engn, Seoul, South Korea
关键词
Intelligent assistive services; Ubiquitous robotics; Multi-agents; Ontologies; Symbolic modeling and reasoning; PEOPLE;
D O I
10.1016/j.robot.2014.10.022
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Ambient Intelligence (AmI) technologies have the potential to create intelligent environments with new generation of assistive services, enhanced with ubiquitous robots. These environments have the ability to be anticipatory, responsive and intelligent providers of assistive services anytime and anywhere. These services can assist frail persons effectively in their daily tasks. One of the main challenging research problems in assistive robotics is to endow ubiquitous robots with ability to pro-actively taking on some tasks to help humans in performing complex activities, by participating with them just as other humans do, in normal societies or organizations. In this paper, we propose a collective intelligence framework based on narrative reasoning and natural language processing. In the proposed approach, we propose a hybrid model that bridges together the Narrative Knowledge Representation Language (NKRL), from natural language processing field, and the HARMS (Humans, software Agents, Robots, Machines and Sensors) model, from multi-agent systems engineering field. This model is able to (i) drive the dialogues between humans, robots and smart devices, (ii) understand a complex situation, and (iii) trigger reactive actions, in the ubiquitous environment, according to given contexts. Two scenarios dedicated to the assistance of a frail person in a smart home equipped with a companion robot and smart objects are implemented and discussed for validation purposes of the proposed framework. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:17 / 27
页数:11
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