Advertisement

Characterizing Environmental Information for Monitoring Agents

  • Albert Esterline
  • Bhanu Gandluri
  • Mannur Sundaresan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3825)

Abstract

A multiagent architecture for vehicle and structural health monitoring is proposed. A prototype using this architecture was developed using JADE. Critical aspects of the design were verified using the SPIN model checker. The tasks in our framework are related to data-fusion levels and Gibson’s realist position on direct perception of objects and affordances. We show how a system consisting of a multiagent system along with the monitored platform exhibits behavior at several levels, from the physics of acoustic emissions to inter-agent conversations expressing desires and beliefs. Communication, perception of public events, and system design conspire to provide the common knowledge needed to coordinate diagnostic tasks.

Keywords

Acoustic Emission Multiagent System Acoustic Emission Signal Structural Health Monitoring Linear Time Temporal Logic 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Garga, A., Campbell, R., Byington, C., Kasmala, G., Lang, D., Lebold, M., Banks, J.: Diagnostic reasoning agents development for HUMS systems. In: American Helicopter Society 57th Annual Forum, Washington, D.C, pp. 1268–1275 (2001)Google Scholar
  2. 2.
    Garga, A., McClintic, K., Campbell, R., Yang, C., Lebold, M.: Hybrid reasoning for prognostic learning in CBM systems. In: IEEE Aerospace Conference, pp. 2957–2969. Big Sky, MT (2001)Google Scholar
  3. 3.
    Roemer, M., Vachtsenavos, G., Byington, C., Kacprzynski, G.: Two-day PHM/ CBM design course, Orlando, FL (2004)Google Scholar
  4. 4.
    Faas, P., Schroeder, J., Smith, G.: Vehicle health management research for legacy and future operational environments. Aerospace and Electronic Systems Magazine IEEE 17(4), 10–16 (2002)CrossRefGoogle Scholar
  5. 5.
    Esterline, A., Gandluri, B., Sundaresan, M., Sankar, J.: Verified models of multiagent systems for vehicle health management. In: 12th SPIE Annual International Symposium on Smart Structures and Materials, San Diego, CA (2005)Google Scholar
  6. 6.
    Wooldridge, M.: Intelligent Agents. In: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, pp. 27–78. MIT press, Cambridge (2000)Google Scholar
  7. 7.
    Huhns, M., Stephens, L.: Multiagent Systems and Societies of Agents. In: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, pp. 79–120. MIT Press, Cambridge (2000)Google Scholar
  8. 8.
    Mabry, S., Schneringer, T., Etters, T., Edwards, N.: Intelligent agents for patient monitoring and diagnostics. In: Proceedings of 2003 ACM Symposium on Applied Computing, Melbourne, FL, pp. 257–262 (2003)Google Scholar
  9. 9.
    Lam, S., Cao, J., Fan, H.: Development of an intelligent agent for airport gate assignment. Journal of Airport Transportation 7(2), 103–114 (2002)Google Scholar
  10. 10.
    Logan, K.: Prognostic software agents for machinery health monitoring. In: IEEE Aerospace Conference, pp. 3213–3225 (2003)Google Scholar
  11. 11.
    Franke, J., Satterfield, B., Czajkowski, M., Jameson, S.: Self-awareness for vehicle safety and mission success. Technical report, Lockheed Martin Advanced Technology Laboratories, Camden, NJ (2002)Google Scholar
  12. 12.
    Smith, R.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers C-29(12), 1104–1113 (1980)CrossRefGoogle Scholar
  13. 13.
    Shen, W., Norrie, D.: An agent-based approach for dynamic manufacturing scheduling. In: Nwana, H., Ndumu, D. (eds.) 3rd Int. Conf. on the Practical Applications of Agents and Multi-Agent Systems, London, UK, pp. 533–548 (1998)Google Scholar
  14. 14.
    FIPA: Welcome to FIPA! (2005), http://www.fipa.org/
  15. 15.
    TILab: Java agent development environment (2005), http://jade.tilab.com/
  16. 16.
    Mller, S.: JMatLink: Matlab Java classes (2005), http://www.held-mueller.de/JMatLink
  17. 17.
    Holzmann, G.: The SPIN Model Checker: Primer and Reference Manual. Addison- Wesley, Boston (2004)Google Scholar
  18. 18.
    Sundaresan, M., Nkrumah, F., Grandhi, G., Derriso, M.: Identification of failure modes in composite materials using a continuous ae sensor system. In: IMECE 2004: ASME Int. Mechanical Engineering Congress, Anaheim, CA (2004)Google Scholar
  19. 19.
    Steinberg, A., Bowman, C.: Revisions to the JDL Data Fusion Model. In: Handbook of Multisensor Data Fusion, pp. 2–1–8. CRC Press, Washington (2001)Google Scholar
  20. 20.
    Elmenreich, W.: Sensor Fusion in Time-Triggered Systems. PhD thesis, Technical University of Vienna, Vienna, Austria (2002)Google Scholar
  21. 21.
    Gibson, J.: The Ecological Approach to Visual Perception. Lawrence Erlbaum Associates, Hillsdale (1986)Google Scholar
  22. 22.
    Dennet, D.: True Believers: The Intentional Strategy and Why It Works. In: The Intentional Stance, pp. 13–35. The MIT Press, Cambridge (1987)Google Scholar
  23. 23.
    Fagin, R., Halpern, J.Y., Moses, Y., Vardi, M.Y.: Reasoning About Knowledge. MIT Press, Cambridge (2003)MATHGoogle Scholar
  24. 24.
    Clark, H., Carlson, T.: Speech Acts and Hearers’ Beliefs. In: Mutual Knowledge, pp. 1–36. Academic Press, London (1982)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Albert Esterline
    • 1
  • Bhanu Gandluri
    • 2
  • Mannur Sundaresan
    • 2
  1. 1.Department of Computer ScienceUSA
  2. 2.Department of Mechanical and Chemical EngineeringNorth Carolina A&T State UniversityGreensboro

Personalised recommendations