Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Understanding Technology in Domestic Environments: Lessons for Cooperative Buildings
CoBuild '98 Proceedings of the First International Workshop on Cooperative Buildings, Integrating Information, Organization, and Architecture
At Home with Ubiquitous Computing: Seven Challenges
UbiComp '01 Proceedings of the 3rd international conference on Ubiquitous Computing
A survey of research on context-aware homes
ACSW Frontiers '03 Proceedings of the Australasian information security workshop conference on ACSW frontiers 2003 - Volume 21
The computer for the 21st Century
IEEE Pervasive Computing
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Towards an Affective Aware Home
ICOST '09 Proceedings of the 7th International Conference on Smart Homes and Health Telematics: Ambient Assistive Health and Wellness Management in the Heart of the City
Environmental user-preference learning for smart homes: An autonomous approach
Journal of Ambient Intelligence and Smart Environments
An intelligent building that listens to your needs
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Building intelligent environment is one of crucial challenges for ubiquitous computing developers. To make the environment adapt rationally according to the desire of users, the system should be able to guess users' interest, by learning users' behavior, habit or preference. While learning the user preference, dealing with uncertainty and conflict resolution is of the utmost importance. When many users are involved in a ubiquitous environment, the decisions of one user can be affected by the desires of others. This makes learning and prediction of user preference difficult. To address the issue, we propose an approach of user preference learning which can be used widely in context-aware systems. We use Bayesian RN-Metanetwork, a multilevel Bayesian network to model user preference and priority.