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Managing Adaptive Versatile environments

Published: 01 December 2005 Publication History

Abstract

The goal of the MavHome project is to develop technologies to Manage Adaptive Versatile environments. In this paper, we present a complete agent architecture for a single inhabitant intelligent environment and discuss the development, deployment, and techniques utilized in our working intelligent environments. Empirical evaluation of our approach has proven its effectiveness at reducing inhabitant interactions in simulated and real environments.

References

[1]
Das, S.K., Cook, D.J., Bhattacharya, A., Heierman III, E.O. and Lin, T.-Y., The role of prediction algorithms in the MavHome smart home architecture. IEEE Wireless Communications. v9 i6. 77-84.
[2]
L.P. Kaelbling, M.L. Littman, A.R. Cassandra, Planning and acting in partially observable stochastic domains, Tech. Rep. CS-96-08, Brown University, Providence, RI, 1996
[3]
Russell, S.J. and Norvig, P., Artificial Intelligence: A Modern Approach. 1995. Prentice Hall, Upper Saddle River, NJ.
[4]
Fine, S., Singer, Y. and Tishby, N., The hierarchical hidden Markov model: Analysis and applications. Machine Learning. v32 i1. 41-62.
[5]
G. Theocharous, K. Rohanimanesh, S. Mahadevan, Learning hierarchical partially observable Markov decision processes for robot navigation, in: IEEE Conference on Robotics and Automation, 2001
[6]
E.O. Heierman, Using information-theoretic principles to discover interesting episodes in a time-ordered input sequence, Ph.D. Thesis, The University of Texas at Arlington, 2004
[7]
http://www.zeroconf.org
[8]
Sutton, R.S. and Barto, A.G., Reinforcement Learning: An Introduction. 1998. MIT Press, Cambridge, MA.
[9]
E. Heierman, D.J. Cook, Improving home automation by discovering regularly occurring device usage patterns, in: Proceedings of the International Conference on Data Mining, 2003, pp. 537-540
[10]
Rissanen, J., Stochastic Complexity in Statistical Inquiry. 1989. World Scientific Publishing Company.
[11]
K. Gopalratnam, D.J. Cook, Active LeZi: An incremental parsing algorithm for device usage prediction in the smart home, in: Proceedings of the Florida Artificial Intelligence Research Symposium, 2003, pp. 38-42
[12]
Batschelet, E., Circular Statistics in Biology. 1981. Academic Press.
[13]
http://aire.csail.mit.edu/projects.shtml
[14]
http://www.amigo-project.org
[15]
http://iwork.stanford.edu
[16]
http://www-lce.eng.cam.ac.uk/research/?view=2&id=7
[17]
http://gaia.cs.uiuc.edu
[18]
The Gator Tech Smart House: A Programmable Pervasive Space
[19]
http://www.cc.gatech.edu/fce/ahri
[20]
http://www.media.mit.edu/research/index.html
[21]
http://www-106.ibm.com/developerworks/wireless/library/wi-pvc
[22]
http://research.microsoft.com/easyliving
[23]
http://www.btexact.com/research/researchprojects/currentresearch?doc=42834
[24]
http://www.cisco.com/warp/public/3/uk/ihome
[25]
http://www.intel.com/research/exploratory/digital_home.htm
[26]
http://www.siemens-industry.co.uk/main/business%20groups/et/smart%20homes
[27]
http://www.10meters.com/homelab1.html
[28]
http://www.accenture.com/xd/xd.asp?it=enweb&xd=services/technology/research/i%hs/room_future.xml
[29]
http://marc.med.virginia.edu/projects_smarthomemonitor.html
[30]
http://www.rgu.ac.uk/sss/research/page.cfm?pge=2546
[31]
M. Mozer, The adaptive house. Website: http://www.cs.colorado.edu/~mozer/house
[32]
Mozer, M.C., Lessons from an adaptive house. In: Smart Environments: Technology, Protocols, and Applications, J. Wiley & Sons, Hoboken, NJ. pp. 273-294.
[33]
Mozer, M., An intelligent environment must be adaptive. IEEE Intelligent Systems. v14 i2. 11-13.
[34]
D.J. Cook, M. Youngblood, E. Heierman, K. Gopalratnam, S. Rao, A. Litvin, F. Khawaja, MavHome: An agent-based smart home, in: Proceedings of the IEEE International Conference on Pervasive Computing and Communications, 2003, pp. 521-524
[35]
http://cswww.essex.ac.uk/intelligent-buildings
[36]
Creating an Ambient-Intelligence Environment Using Embedded Agents
[37]
http://architecture.mit.edu/house_n
[38]
http://www2.nict.go.jp/jt/a135/eng
[39]
http://www.equator.ac.uk/index.php
[40]
http://www.inria.fr/recherche/equipes_ur/prima.en.html

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2005

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  1. Adaptive versatile environments
  2. MavHome project

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  • (2016)Mining sequential patterns to efficiently manage Energy Storage Systems within smart home buildingsJournal of Ambient Intelligence and Smart Environments10.3233/AIS-1603818:3(287-300)Online publication date: 1-Jan-2016
  • (2013)Inferring ECA-based rules for ambient intelligence using evolutionary feature extractionJournal of Ambient Intelligence and Smart Environments10.5555/2594719.25947225:6(563-587)Online publication date: 1-Nov-2013
  • (2013)Learning user preferences for adaptive pervasive environmentsACM Transactions on Autonomous and Adaptive Systems10.1145/2451248.24512538:1(1-26)Online publication date: 19-Apr-2013
  • (2011)Exploitational interactionComputing with instinct10.5555/1980745.1980756(119-142)Online publication date: 1-Jan-2011
  • (2011)Learning Algorithm for Human Activity Detection in Smart EnvironmentsProceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 0310.1109/WI-IAT.2011.80(45-48)Online publication date: 22-Aug-2011
  • (2011)A model for using machine learning in smart environmentsProceedings of the 6th international conference on Grid and Pervasive Computing10.1007/978-3-642-27916-4_4(24-33)Online publication date: 11-May-2011
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