Automatic Personal Preference Learning System in Intelligent e-Home
Date Issued
2004
Date
2004
Author(s)
Chen, Li-Ming
DOI
en-US
Abstract
In this thesis, the Automatic Personal Preference Learning System (APPLE system) is proposed. The APPLE system is able to learn personal preference (or inhabitant preference) and then feedback a proper service to the inhabitant.
First of all, the complete database is needed before learning the personal preference. Thus we need the data collection mechanism to collect all these information. There are two kinds of data have close relationships with the personal preference; that is the environment data and the personal preference data, which we call “e-Home Data.” In addition, this thesis adopts Device Control Agent both to connect sensors with home electric appliances, and to collect environment data through different type of sensors. The other e-Home data is personal preference data, which records all electric appliances using condition operated by people. Personal preference data can support the APPLE system to find out personal preference precisely. This thesis proposes Interface Agent to collect personal preference data.
However, the e-Home data collection mechanism sometimes receives dirty data during collection. Too much dirty data will seriously affect the accuracy of APPLE system. In order to avoid the situation, the APPLE system uses DCA (detection and correction algorithm) and RMA (removal and merging algorithm) to handle those dirty data.
After collecting e-Home data, the APPLE system use Personal Preference Belief Network to predict the personal preference. The system is evaluated through experiments, and the accuracy of predicting personal preference is over 90% when the APPLE system has learned after a period of time.
Subjects
自動化個人偏好學習系統
個人偏好學習
蘋果系統
家庭自動化
環境資料收集系統
電子家庭
智慧型環境
home automation
intelligent environment
automatic personal preference learning system
APPLE system
intelligent e-Home
e-Home
personal preference learning
environment data collection system
Type
thesis
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