NDLI: An Incremental Adaptive Life Long Learning Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments

Content Provider	IEEE Xplore Digital Library
Author	Hagras, H. Doctor, F. Callaghan, V. Lopez, A.
Copyright Year	1993
Abstract	In this paper, we present a novel type-2 fuzzy systems based adaptive architecture for agents embedded in ambient intelligent environments (AIEs). Type-2 fuzzy systems are able to handle the different sources of uncertainty and imprecision encountered in AIEs to give a very good response. The presented agent architecture uses a one pass method to learn in a nonintrusive manner the user's particular behaviors and preferences for controlling the AIE. The agent learns the user's behavior by learning his particular rules and interval type-2 Membership Functions (MFs), these rules and MFs can then be adapted online incrementally in a lifelong learning mode to suit the changing environmental conditions and user preferences. We will show that the type-2 agents generated by our one pass learning technique outperforms those generated by genetic algorithms (GAs). We will present unique experiments carried out by different users over the course of the year in the Essex Intelligent Dormitory (iDorm), which is a real AIE test bed. We will show how the type-2 agents learnt and adapted to the occupant's behavior whilst handling the encountered short term and long term uncertainties to give a very good performance that outperformed the type-1 agents while using smaller rule bases
Sponsorship	IEEE Computational Intelligence Society
Starting Page	41
Ending Page	55
Page Count	15
File Size	3046440
File Format	PDF
ISSN	10636706
Volume Number	15
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Continuing professional development Intelligent agent Ambient intelligence Uncertainty Fuzzy systems Computational intelligence Embedded computing Pervasive computing Humans Adaptive systems learning Ambient intelligent environment embedded agents interval type-2 fuzzy systems
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Control and Systems Engineering Computational Theory and Mathematics

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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