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Incremental Learning of Daily Routines as Workflows in a Smart Home Environment

Incremental Learning of Daily Routines as Workflows in a Smart Home Environment Incremental Learning of Daily Routines as Workflows in a Smart Home Environment BERARDINA DE CAROLIS, STEFANO FERILLI, and DOMENICO REDAVID, University of Bari Smart home environments should proactively support users in their activities, anticipating their needs according to their preferences. Understanding what the user is doing in the environment is important for adapting the environment's behavior, as well as for identifying situations that could be problematic for the user. Enabling the environment to exploit models of the user's most common behaviors is an important step toward this objective. In particular, models of the daily routines of a user can be exploited not only for predicting his/her needs, but also for comparing the actual situation at a given moment with the expected one, in order to detect anomalies in his/her behavior. While manually setting up process models in business and factory environments may be cost-effective, building models of the processes involved in people's everyday life is infeasible. This fact fully justifies the interest of the Ambient Intelligence community in automatically learning such models from examples of actual behavior. Incremental adaptation of the models and the ability to express/learn complex conditions on the involved tasks are also desirable. This article http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Incremental Learning of Daily Routines as Workflows in a Smart Home Environment

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
2160-6455
DOI
10.1145/2675063
Publisher site
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Abstract

Incremental Learning of Daily Routines as Workflows in a Smart Home Environment BERARDINA DE CAROLIS, STEFANO FERILLI, and DOMENICO REDAVID, University of Bari Smart home environments should proactively support users in their activities, anticipating their needs according to their preferences. Understanding what the user is doing in the environment is important for adapting the environment's behavior, as well as for identifying situations that could be problematic for the user. Enabling the environment to exploit models of the user's most common behaviors is an important step toward this objective. In particular, models of the daily routines of a user can be exploited not only for predicting his/her needs, but also for comparing the actual situation at a given moment with the expected one, in order to detect anomalies in his/her behavior. While manually setting up process models in business and factory environments may be cost-effective, building models of the processes involved in people's everyday life is infeasible. This fact fully justifies the interest of the Ambient Intelligence community in automatically learning such models from examples of actual behavior. Incremental adaptation of the models and the ability to express/learn complex conditions on the involved tasks are also desirable. This article

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS) – Association for Computing Machinery

Published: Jan 28, 2015

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