An intelligent lighting system: learn user preferences from inconsistent feedback

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Abstract

In this paper, we propose an intelligent lighting system that is able to learn the most preferred light level of a user by iteratively interacting with the user. In each iteration, the system offers the user a certain amount of light and the user gives his feedback to the system. When the user is satisfied, implicit feedback is given by not intervening the system. Otherwise explicit feedback such as "too bright" or "too dark" will be given. Each user feedback is modeled as a probabilistic event, which means that it is possible that sometimes the user will give different feedback for the same light level. To avoid disturbing the user to much, we propose a learning algorithm which learns the most preferred light level of the user with only a few explicit interventions.
Original languageEnglish
Title of host publicationProceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages1620-1626
Number of pages7
ISBN (Print)978-1-4503-4462-3
DOIs
Publication statusPublished - 2016
Event2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) - Heidelberg, Germany
Duration: 12 Sep 201616 Sep 2016
http://ubicomp.org/ubicomp2016/index.php

Conference

Conference2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016)
Abbreviated titleUbiComp2016
CountryGermany
CityHeidelberg
Period12/09/1616/09/16
Internet address

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