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Supervised learning of an abstract context model for an intelligent environment

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sOc-EUSAI '05: Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies

Pages 259 - 264

https://doi.org/10.1145/1107548.1107612

Published: 12 October 2005 Publication History

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Abstract

This paper addresses the problem of supervised learning in intelligent environments. An intelligent environment perceives user activity and offers a number of services according to the perceived information about the user. An abstract context model in the form of a situation network is used to represent the intelligent environment, its occupants and their activities. The context model consists of situations, roles played by entities and relations between these entities. The objective is to adapt the system services, which are associated to the situations of the model, to the changing needs of the user. For this, a supervisor gives feedback by correcting system services that are found to be inappropriate to user needs. The situation network can be developed by exchanging the system service-situation association, by splitting the situation, or by learning new roles. The situation split is interpreted as a replacement of the former situation by sub-situations whose number and characteristics are determined using conceptual or decision tree algorithms. Different algorithms have been tested on a context model within the SmartOffice environment of the PRIMA research group. The decision tree algorithm (ID3) has been found to give the best results.

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Negrete Ramírez JCardinale Y(2022)Activities of Daily Living Detection on Healthcare: A CategorizationProceedings of the 7th International Workshop on Sensor-based Activity Recognition and Artificial Intelligence10.1145/3558884.3558887(1-10)Online publication date: 19-Sep-2022
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Sezer ODogdu EOzbayoglu A(2018)Context-Aware Computing, Learning, and Big Data in Internet of Things: A SurveyIEEE Internet of Things Journal10.1109/JIOT.2017.27736005:1(1-27)Online publication date: Feb-2018
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Published In

sOc-EUSAI '05: Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies

October 2005

316 pages

ISBN:1595933042

DOI:10.1145/1107548

Conference Chairs:
Gerard Bailly
CNRS, France
,
James L. Crowley
INPG - France

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 12 October 2005

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sOc-EUSAI05: Smart Objects & Ambient Intelligence

October 12 - 14, 2005

Grenoble, France

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Negrete Ramírez JCardinale Y(2022)Activities of Daily Living Detection on Healthcare: A CategorizationProceedings of the 7th International Workshop on Sensor-based Activity Recognition and Artificial Intelligence10.1145/3558884.3558887(1-10)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3558884.3558887
Sezer ODogdu EOzbayoglu A(2018)Context-Aware Computing, Learning, and Big Data in Internet of Things: A SurveyIEEE Internet of Things Journal10.1109/JIOT.2017.27736005:1(1-27)Online publication date: Feb-2018
https://doi.org/10.1109/JIOT.2017.2773600
Suraj Kool IKumar DBarma S(2018)Visual Machine Intelligence for Home Automation2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU)10.1109/IoT-SIU.2018.8519915(1-6)Online publication date: Feb-2018
https://doi.org/10.1109/IoT-SIU.2018.8519915
Kapoor SSingh AGoswami UKumar SChitranshi G(2016)Design and implementation of a robust system for recognizing alphabets using artifical neural network2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO.2016.7785026(606-609)Online publication date: Sep-2016
https://doi.org/10.1109/ICRITO.2016.7785026
Thaduri AKumar UVerma A(2014)Computational intelligence framework for context-aware decision makingInternational Journal of System Assurance Engineering and Management10.1007/s13198-014-0320-88:S4(2146-2157)Online publication date: 4-Dec-2014
https://doi.org/10.1007/s13198-014-0320-8
Dunwell IPetridis PHendrix MArnab SAL-Smadi MGuetl C(2012)Guiding Intuitive Learning in Serious GamesProceedings of the 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS)10.1109/CISIS.2012.205(911-916)Online publication date: 4-Jul-2012
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García-Herranz MAlamán XHaya P(2011)Exploitational interactionComputing with instinct10.5555/1980745.1980756(119-142)Online publication date: 1-Jan-2011
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Lee HSon BKim JLee S(2011)Comparison Evidential Fusion Network with Decision Tree for Reliable Contextual InformationAdvances in Automation and Robotics, Vol.110.1007/978-3-642-25553-3_63(513-520)Online publication date: 2011
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García–Herranz MAlamán XHaya P(2011)Exploitational InteractionComputing with Instinct10.1007/978-3-642-19757-4_8(119-142)Online publication date: 2011
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Brdiczka O(2010)Integral framework for acquiring and evolving situations in smart environmentsJournal of Ambient Intelligence and Smart Environments10.5555/1804772.18047772:2(91-108)Online publication date: 1-Apr-2010
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Automatic Development of an Abstract Context Model for an Intelligent Environment

A consolidated view of context for intelligent systems

Capturing Context Information in a Context Aware Virtual Environment

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