Learning automation policies for pervasive computing environments

Brian D. Ziebart, Dan Roth, R H Campbell, Anind K. Dey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

If current trends in cellular phone technology, personal digital assistants, and wireless networking are indicative of the future, we can expect our environments to contain an abundance of networked computational devices and resources. We envision these devices acting in an orchestrated manner to meet users' needs, pushing the level of interaction away from particular devices and towards interactions with the environment as a whole. Computation will be based not only on input explicitly provided by the user, but also on contextual information passively collected by networked sensing devices. Configuring the desired responses to different situations will need to be easy for users. However, we anticipate that the triggering situations for many desired automation policies will be complex, unforeseen functions of low-level contextual information. This is problematic since users, though easily able to perceive triggering situations, will not be able to define them as functions of the devices' available contextual information, even when such a function (or a close approximation) does exist. In this paper, we present an alternative approach for specifying the automation rules of a pervasive computing environment using machine learning techniques. Using this approach, users generate training data for an automation policy through demonstration, and, after training is completed, a learned function is employed for future automation. This approach enables users to automate the environment based on changes in the environment that are complex, unforeseen combinations of contextual information. We developed our learning service within Gaia, our pervasive computing system, and deployed it within our prototype pervasive computing environment. Using the system, we were able to have users demonstrate how sound and lighting controls should adjust to different applications used within the environment, the users present, and the locations of those users and then automate those demonstrated preferences.

Original languageEnglish (US)
Title of host publicationProceedings - Second International Conference on Autonomic Computing, ICAC 2005
Pages193-203
Number of pages11
DOIs
StatePublished - Dec 1 2005
Event2nd International Conference on Autonomic Computing, ICAC 2005 - Seattle, WA, United States
Duration: Jun 13 2005Jun 16 2005

Publication series

NameProceedings - Second International Conference on Autonomic Computing, ICAC 2005
Volume2005

Other

Other2nd International Conference on Autonomic Computing, ICAC 2005
CountryUnited States
CitySeattle, WA
Period6/13/056/16/05

ASJC Scopus subject areas

  • Engineering(all)

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