Multi-user Preference Model via Layered Bayesian Networksin a Smart Home Environment
Date Issued
2007
Date
2007
Author(s)
Lin, Zhi-Hao
DOI
zh-TW
Abstract
An important issue to be addressed in a smart home environment is how to provide appropriate services according to the preference of inhabitants. In this paper, we aim at developing a system to learn a multiple users’ preference model that represents relationships among users as well as dependency between services and sensor observations. Thus, the service can be inferred based on the learnt model. To achieve this, we propose a three-layer model in our work. At the first layer, raw data from sensors are interpreted as context information after noise removal. The second layer is dynamic Bayesian networks which model the observation sequences including inhabitants’ location and electrical appliance information. At the highest layer, we integrate the results of the second layer, environment information and the relations between inhabitants to recommend the service to inhabitants. Therefore, the system can infer appropriate services to inhabitants at right time and right place and let them feel comfortable. In experiments, we show that our model can reliably recommend and precise services to inhabitants in a smart home environment.
Subjects
智慧家庭
家庭自動化
偏好學習
服務提供
多人環境
Smart Home
Home Automation
Preference modeling
Service rovision
Multi-user Environment
Type
thesis
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