Provenance of Decisions in Emergency Response Environments

  • Iman Naja
  • Luc Moreau
  • Alex Rogers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6378)


Mitigating the devastating ramifications of major disasters requires emergency workers to respond in a maximally efficient way. Information systems can improve their efficiency by organizing their efforts and automating many of their decisions. However, absence of documenting how decisions were made by the system prevents decisions from being reviewed to check the reasons for their making or their compliance with policies. We apply the concept of provenance to decision making in emergency response situations and use the Open Provenance Model to express provenance produced in RoboCup Rescue Simulation. We produce provenance DAGs using a novel OPM profile that conceptualizes decisions in the context of emergency response. Finally, we traverse the OPM DAGs to answer some provenance questions about those decisions.


MultiAgent System Emergency Response Provenance Information Emergency Responder Emergency Response System 
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.


  1. 1.
  2. 2.
    Bose, R., Frew, J.: Lineage retrieval for scientific data processing: a survey. ACM Computing Surveys 37(1), 1–28Google Scholar
  3. 3.
    Braun, U., Holland, D.A., Muniswamy-Reddy, K.K., Seltzer, M.I.: Coping with cycles in provenance. Technical report, Harvard University (2006)Google Scholar
  4. 4.
    Chalkiadakis, G., Boutilier, C.: Sequential decision making in repeated coalition formation under uncertainty. In: Proc. of AAMAS 2008, Estoril, Portugal, (2008)Google Scholar
  5. 5.
    Chapman, A., Jagadish, H.V.: Why not? In: Proc. of the 35th SIGMOD int’l conf. on Management of dataGoogle Scholar
  6. 6.
    Chorley, A., Edwards, P., Preece, A., Farrington, J.: Tools for tracing evidence in social science. In: Third Int’l Conf. on e-Social Science (October 2007)Google Scholar
  7. 7.
    Fullam, K.K., Barber, K.S.: Dynamically learning sources of trust information: experience vs. reputation. In: Proc. of AAMAS 2007, (2007)Google Scholar
  8. 8.
    Georgeff, M., Pell, B., Pollack, M., Tambe, M., Wooldridge, M.: The Belief-Desire-Intention Model of Agency. In: Proc. of the 5th Int’l Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages (1998)Google Scholar
  9. 9.
    Hasan, R., Sion, R., Winslett, M.: The case of the fake picasso: Preventing history forgery with secure provenance. In: Proc. of 7th USENIX Conference on File and Storage Technologies, FAST 2009, pp. 1–14 (2009)Google Scholar
  10. 10.
    Khosravifar, B., Gomrokchi, M., Bentahar, J., Thiran, P.: Maintenance-based trust for multi-agent systems. In: Proc. of AAMAS 2009, Budapest, Hungary, (2009)Google Scholar
  11. 11.
    Kifor, T., Varga, L.Z., Vazquez-Salceda, J., Alvarez, S., Willmott, S., Miles, S., Moreau, L.: Provenance in agent-mediated healthcare systems. IEEE Intelligent Systems 21, 38–46 (2006)CrossRefGoogle Scholar
  12. 12.
    Kota, R., Gibbins, N., Jennings, N.R.: Self-organising agent organisations. In: Proc. of AAMAS 2009, Budapest, Hungary, (2009)Google Scholar
  13. 13.
    Miles, S.: Electronically querying for the provenance of entities. In: Moreau, L., Foster, I. (eds.) IPAW 2006. LNCS, vol. 4145, pp. 184–192. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Miles, S., Groth, P., Munroe, S., Moreau, L.: Prime: A methodology for developing provenance-aware applications. ACM TOSEM (2010)Google Scholar
  15. 15.
    Miles, S., Munroe, S., Luck, M., Moreau, L.: Modelling the provenance of data in autonomous systems. In: Proc. of AAMAS 2007, (2007)Google Scholar
  16. 16.
    Moreau, L.: The foundations for provenance on the web. Foundations and Trends in Web Science (in Press 2010) Google Scholar
  17. 17.
    Moreau, L., Clifford, B., Freire, J., Futrelle, J., Gil, Y., Groth, P., Kwasnikowska, N., Miles, S., Missier, P., Myers, J., Plale, B., Simmhan, Y.L., Stephan, E., Van Den Bussche, J.: The Open Provenance Model Core Specification (v1.1). Future Generation Computer Systems (in Press 2010) Google Scholar
  18. 18.
    Papamichai, K.N., French, S.: Explaining and justifying the advice of a decision support system: a natural language generation approach. Expert Systems with Applications 24 (2003)Google Scholar
  19. 19.
    Phung, T., Winikoff, M., Padgham, L.: Learning Within the BDI Framework: An Empirical Analysis, pp. 282–288 (2005)Google Scholar
  20. 20.
    Rao, A.S., Georgeff, M.P.: BDI Agents: From Theory to Practice. In: Proceedings of the First International Conference on MultiAgent Systems (1995)Google Scholar
  21. 21.
    Ringelstein, C., Staab, S.: PAPEL: A Language and Model for Provenance-Aware Policy Definition and Execution. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. 22.
    Simmhan, Y.L., Plale, B., Gannon, D.: A survey of data provenance in e-science. SIGMOD Rec. 34, 31–36 (2005)CrossRefGoogle Scholar
  23. 23.
    Teacy, W.T., Patel, J., Jennings, N.R., Luck, M.: Travos: Trust and reputation in the context of inaccurate information sources. Autonomous Agents and Multi-Agent Systems 12, 183–198 (2006)CrossRefGoogle Scholar
  24. 24.
    W3C Provenance Incubator Group: (Provenance dimensions) (last accessed March 01 2010)
  25. 25.
    Weitzner, D.J., Abelson, H., Berners-Lee, T., Feigenbaum, J., Hendler, J., Sussman, G.J.: Information accountability. Commun. ACM 51, 82–87 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Iman Naja
    • 1
  • Luc Moreau
    • 1
  • Alex Rogers
    • 1
  1. 1.School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK

Personalised recommendations