Hardware simulation of home automation using pattern matching and reinforcement learning for disabled people

M. B. I. Reaz, A. Assim, M. I. Ibrahimy, F. Choong, F. Mohd-Yasin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Future Smart-Home device usage prediction is a very important module in artificial intelligence. The technique involves analyzing the user performed actions history and apply mathematical methods to predict the most feasible next user action. Unfortunately most of the techniques tend to ignore the adaptation to the user preferred actions and the relation between the actions and the state of the environment which is not practical for Smart-Home systems. This paper present a new algorithm of user action prediction based on pattern matching and techniques of reinforcement learning. The algorithm is modeled using hardware description language VHDL. Synthetic data had been used to test the algorithm and the result shows that the algorithm performs realistically better than the current available techniques.

Original languageEnglish
Title of host publicationProceedings of the 2008 International Conference on Artificial Intelligence, ICAI 2008 and Proceedings of the 2008 International Conference on Machine Learning; Models, Technologies and Applications
PublisherCSREA
Pages213-218
Number of pages6
ISBN (Print)9781601320728
Publication statusPublished - 2008
Event2008 International Conference on Artificial Intelligence and 2008 International Conference on Machine Learning; Models, Technologies and Applications - Las Vegas, NV, United States
Duration: 14 Jul 200817 Jul 2008

Conference

Conference2008 International Conference on Artificial Intelligence and 2008 International Conference on Machine Learning; Models, Technologies and Applications
Abbreviated titleICAI 2008 and MLMTA 2008
Country/TerritoryUnited States
CityLas Vegas, NV
Period14/07/0817/07/08

Keywords

  • Action prediction
  • Multi-agent system
  • Reinforcement learning
  • Smart home

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Software
  • Computer Science Applications

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