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A Multi-agent Approach to Controlling a Smart Environment

  • Diane J. Cook
  • Michael Youngblood
  • Sajal K. Das
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4008)

Abstract

The goal of the MavHome (Managing An Intelligent Versa- tile Home) project is to create a home that acts as a rational agent. The agent seeks to maximize inhabitant comfort and minimize operation cost. In order to achieve these goals, the agent must be able to predict the mobility patterns and device usages of the inhabitants. Because of the size of the problem, controlling a smart environment can be effectively approached as a multi-agent task. Individual agents can address a portion of the problem but must coordinate their actions to accomplish the overall goals of the system. In this chapter, we discuss the application of multi-agent systems to the challenge of controlling a smart environment and describe its implementation in the MavHome project.

Keywords

Multiagent System Session Initiation Protocol Smart Home Minimum Description Length Partially Observable Markov Decision Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Das, S.K., Cook, D.J.: Health monitoring in an agent-based smart home by activity predition. In: Zhang, D., Mokhari, M. (eds.) Toward a Human-Friendly Assistive Environment, pp. 3–14. IOS Press, Amsterdam (2004)Google Scholar
  2. 2.
    AIRE Group: MIT Project AIRE – About Us (2004), http://www.ai.mit.edu/projects/aire
  3. 3.
    Fox, A., Johanson, B., Hanrahan, P., Winograd, T.: Integrating information appliances into an interactive space. IEEE Computer Graphics and Applications 20, 54–65 (2000)CrossRefGoogle Scholar
  4. 4.
    Romn, M., Hess, C.K., Cerqueira, R., Ranganathan, A., Campbell, R.H., Nahrstedt, K.: Gaia: A middleware infrastructure to enable active spaces. IEEE Pervasive Computing, 74–83 (2002)Google Scholar
  5. 5.
    Abowd, G.D., Mynatt, E.D.: Designing for the human experience in smart environments. In: Cook, D.J., Das, S.K. (eds.) Smart Environments: Technology, Protocols, and Applications, pp. 153–174. Wiley, Chichester (2005)Google Scholar
  6. 6.
    Helal, A., Mann, W., El-Zabadani, H., King, J., Kaddoura, Y., Jansen, E.: The gator tech smart house: A programmable pervasive space. IEEE Computer 38, 50–60 (2005)Google Scholar
  7. 7.
    NIST: Smart space NIST laboratory (2005), http://www.nist.gov/smartspace/
  8. 8.
    Mozer, M.C.: Lessons from an adaptive home. In: Cook, D.J., Das, S.K. (eds.) Smart Environments: Technology, Protocols, and Applications, pp. 273–298. Wiley, Chichester (2005)Google Scholar
  9. 9.
    Hagras, H., Callaghan, V., Colley, M., Clarke, G., Pounds-Cornish, A., Duman, H.: Creating an ambient-intelligence environment using embedded agents. IEEE Intelligent Systems 19 (2004)Google Scholar
  10. 10.
    Adams, J.A.: Multiagent systems: A modern approach to distributed artificial intelligence. AI Magazine 22, 105–108 (2001)Google Scholar
  11. 11.
    Stone, P., Veloso, M.: Multiagent systems: A survey from a machine learning perspective. Autonomous Robots 8, 345–383 (2000)CrossRefGoogle Scholar
  12. 12.
    CSIRO: Intelligent interactive technology (2005), http://www.cmis.csiro.au/iit/
  13. 13.
    Haigh, K.Z., Phelps, J., Geib, C.W.: An open agent architecture for assisting elder independence. In: Proceedings of the First International Joint Conference on autonomous Agents and Multiagent Systems, pp. 578–586 (2002)Google Scholar
  14. 14.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of the 11th International Conference on Data Engineering, pp. 3–14 (1995)Google Scholar
  15. 15.
    Heierman, E.O., Cook, D.J.: Improving home automation by discovering regularly occurring device usage patterns. In: Proceedings of the International Conference on Data Mining (2003)Google Scholar
  16. 16.
    Rissanen, J.: Stochastic Complexity in Statistical inquiry. World Scientific Publishing Company, Singapore (1989)MATHGoogle Scholar
  17. 17.
    Ziv, J., Lempel, A.: Compression of individual sequences via variable rate coding. IEEE Transactions on Information Theory IT-24, 530–536 (1978)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Cielniak, G., Bennewitz, M., Burgard, W.: Where is..? learning and utilizing motion patterns of persons with mobile robots. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pp. 909–914 (2003)Google Scholar
  19. 19.
    Philipose, M., Fishkin, K., Perkowitz, M., Patterson, D., Fox, D., Kautz, H., Hahnel, D.: Inferring activities from interactions with objects. Pervasive Computing 3, 50–56 (2004)CrossRefGoogle Scholar
  20. 20.
    Bell, T.C., Cleary, J.G., Witten, I.H.: Text compression. Prentice Hall, Englewood Cliffs (1990)Google Scholar
  21. 21.
    Gopalratnam, K., Cook, D.J.: Online sequential prediction via incremental parsing: The Active LeZi algorithm. IEEE Intelligent Systems (2005)Google Scholar
  22. 22.
    Pineau, J., Roy, N., Thrun, S.: A Hierarchical Approach to POMDP Planning and Execution. In: Workshop on Hierarchy and Memory in Reinforcement Learning (ICML) (2001)Google Scholar
  23. 23.
    Theocharous, G., Rohanimanesh, K., Mahadevan, S.: Learning Hierarchical Partially Observable Markov Decision Processes for Robot Navigation. In: IEEE Conference on Robotics and Automation (2001)Google Scholar
  24. 24.
    Precup, D., Sutton, R.S.: Multi-time models for temporally abstract planning. Advances in Neural Information Processing Systems 10, 1050–1056 (1997)Google Scholar
  25. 25.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
  26. 26.
    Youngblood, G.M., Holder, L.B., Cook, D.J.: A learning architecture for automating the intelligent environment. In: Proceedings of the Conference on Innovative Applications of Artificial Intelligence (to appear 2005)Google Scholar
  27. 27.
    Cameron, K., Hughes, K., Doughty, K.: Reducing fall incidence in community elders by telecare using predictive systems. In: Proceedings of the International IEEE-EMBS Conference, pp. 1036–1039 (1997)Google Scholar
  28. 28.
    Najafi, B., Aminian, K., Loew, F., Blanc, Y., Robert, P.: Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. IEEE Transactions on Biomedical Engineering 49, 843–851 (2002)CrossRefGoogle Scholar
  29. 29.
    Najafi, B., Aminian, K., Paraschiv-Ionescu, A., Loew, F., Bula, C., Robert, P.: Ambulatory system for human motion analysis using a kinematic sensor: Monitoring of daily physical activity in the elderly. IEEE Transactions on Biomedical Engineering 50, 711–723 (2003)CrossRefGoogle Scholar
  30. 30.
    Kautz, H., Arnstein, L., Borriello, G., Etzioni, O., Fox, D.: An overview of the assisted cognition project. In: Proceedings of the AAAI workshop on automation as caregiver (2002)Google Scholar
  31. 31.
    Pollack, M.E., Brown, L., Colbry, D., McCarthy, C.E., Orosz, C., Peintner, B., Ramakrishnan, S., Tsamardinos, I.: Autoreminder: An intelligent cognitive orthotic system for people with memory impairment. Robotics and Autonomous Systems 44, 273–282 (2003)CrossRefGoogle Scholar
  32. 32.
    Das, S.K., Cook, D.J.: Health monitoring in an agent-based smart home. In: Proceedings of the International Conference on Smart Homes and Health Telematics (ICOST) (2004)Google Scholar
  33. 33.
    Heierman, E.O.: Using information-theoretic principles to discover interesting episodes in a time-ordered sequence. PhD thesis, The University of Texas at Arlington (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Diane J. Cook
    • 1
  • Michael Youngblood
    • 1
  • Sajal K. Das
    • 1
  1. 1.Department of Computer Science EngineeringThe University of Texas at Arlington 

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