Research on design and implementation of the artificial intelligence agent for smart home based on support vector machine

Authors:
Jonghwa Choi;Dongkyoo Shin;Dongil Shin
Affiliations:
Department of Computer Science and Engineering, Sejong University, Seoul, Korea;Department of Computer Science and Engineering, Sejong University, Seoul, Korea;Department of Computer Science and Engineering, Sejong University, Seoul, Korea
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

Citing 3
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
A system architecture for context-aware mobile computing

A system architecture for context-aware mobile computing
Intelligent homes: a new challenge in telecommunications standardization

IEEE Communications Magazine

Modeling Smart Homes for Prediction Algorithms

KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we provide information an artificial intelligence agent for a smart home and discuss a context model for implementation in an efficient smart home. An artificial intelligence agent in a smart home learns about the occupants and the smart environment, and predicts the appliance service that they will want. We propose the SVM (Support Vector Machine) for the learning and prediction aspects of the artificial intelligence agent. The experiment was done using three methods. Each of these three methods applies a higher importance to a different set of context data, out of the data related to the occupant, home environment, and the characteristics of the home appliances. Excellent results were seen when the experiment applied a higher importance to the data related to the characteristics of the home appliances.