Advertisement

Using Risk Analysis to Evaluate Design Alternatives

  • Yudistira Asnar
  • Volha Bryl
  • Paolo Giorgini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4405)

Abstract

Recently, multi-agent systems have proved to be a suitable approach to the development of real-life information systems. In particular, they are used in the domain of safety critical systems where availability and reliability are crucial. For these systems, the ability to mitigate risk (e.g., failures, exceptional events) is very important. In this paper, we propose to incorporate risk concerns into the process of a multi-agent system design and describe the process of exploring and evaluating design alternatives based on risk-related metrics. We illustrate the proposed approach using an Air Traffic Management case study.

Keywords

Planning Domain Goal Model Relaxation Action Fault Tree Analysis Goal Property 
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.
    Truszkowski, W., et al.: Asteroid exploration with autonomic systems. In: Proceedings of the 11th IEEE International Conference and Workshop on the Engineering of Computer-Based Systems, May 2004, pp. 484–489 (2004)Google Scholar
  2. 2.
    Matsui, H., Izumi, K., Noda, I.: Soft-restriction approach for traffic management under disaster rescue situations. In: ATDM’06: 1st Workshop on Agent Technology for Disaster Management (2006)Google Scholar
  3. 3.
    Avizienis, A., et al.: Basic Concepts and Taxonomy of Dependable and Secure Computing. IEEE Trans. Dependable Sec. Comput. 1(1), 11–33 (2004)CrossRefGoogle Scholar
  4. 4.
    Ljungberg, M., Lucas, A.: The OASIS Air-Traffic Management System. In: PRICAI’92: In Proceedings of the Second Pacific Rim International Conference on Artificial Intelligence (1992)Google Scholar
  5. 5.
    Truszkowski, W., et al.: Autonomous and autonomic systems: a paradigm for future space exploration missions. IEEE Transactions on Systems, Man and Cybernetics, Part C 36(3), 279–291 (2006)CrossRefGoogle Scholar
  6. 6.
    Bryl, V., et al.: Designing security requirements models through planning. In: Dubois, E., Pohl, K. (eds.) CAiSE 2006. LNCS, vol. 4001, pp. 33–47. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Bryl, V., Giorgini, P., Mylopoulos, J.: Designing cooperative IS: Exploring and evaluating alternatives. In: CoopIS’06, pp. 533–550 (2006)Google Scholar
  8. 8.
    Weld, D.S.: Recent Advances in AI Planning. AI Magazine 20(2), 93–123 (1999)Google Scholar
  9. 9.
    Peer, J.: Web Service Composition as AI Planning – a Survey. Technical report, University of St. Gallen (2005)Google Scholar
  10. 10.
    LPG Homepage: LPG-td Planner, http://zeus.ing.unibs.it/lpg/
  11. 11.
    Giorgini, P., et al.: Formal Reasoning Techniques for Goal Models. Journal of Data Semantics (October 2003)Google Scholar
  12. 12.
    Sebastiani, R., Giorgini, P., Mylopoulos, J.: Simple and Minimum-Cost Satisfiability for Goal Models. In: Persson, A., Stirna, J. (eds.) CAiSE 2004. LNCS, vol. 3084, pp. 20–33. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Fredriksen, R., et al.: The CORAS framework for a model-based risk management process. In: Anderson, S., Bologna, S., Felici, M. (eds.) SAFECOMP 2002. LNCS, vol. 2434, pp. 94–105. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    DoD: Military Standard, Procedures for Performing a Failure Mode, Effects, and Critical Analysis (MIL-STD-1692A). U.S. Department of Defense (1980)Google Scholar
  15. 15.
    Vesely, W., et al.: Fault Tree Handbook. U.S Nuclear Regulatory Commission (1981)Google Scholar
  16. 16.
    USCG: Risk Based Decision Making Guidelines. (November 2005), http://www.uscg.mil/hq/g-m/risk/e-guidelines/RBDMGuide.htm
  17. 17.
    Feather, M.S.: Towards a Unified Approach to the Representation of, and Reasoning with, Probabilistic Risk Information about Software and its System Interface. In: 15th IEEE International Symposium on Software Reliability Engineering, November 2004, pp. 391–402. IEEE Computer Society, Los Alamitos (2004)CrossRefGoogle Scholar
  18. 18.
    Ghallab, M., et al.: PDDL – The Planning Domain Definition Language. In: Proceedings of the Fourth International Conference on Artificial Intelligence Planning Systems (1998)Google Scholar
  19. 19.
    Edelkamp, S., Hoffmann, J.: PDDL2.2: The language for the classical part of the 4th international planning competition. Technical Report 195, University of Freiburg (2004)Google Scholar
  20. 20.
    IPC-4 Homepage: International Planning Competition (2004), http://ls5-www.cs.uni-dortmund.de/~edelkamp/ipc-4/
  21. 21.
    Anderson, J.S., Fickas, S.: A proposed perspective shift: viewing specification design as a planning problem. In: IWSSD ’89: 5th Int. workshop on Software specification and design, pp. 177–184 (1989)Google Scholar
  22. 22.
    Castillo, L., Fdez-Olivares, J., Gonzalez, A.: Integrating hierarchical and conditional planning techniques into a software design process for automated manufacturing. In: ICAPS 2003, Workshop on Planning under Uncertainty and Incomplete Information, pp. 28–39 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yudistira Asnar
    • 1
  • Volha Bryl
    • 1
  • Paolo Giorgini
    • 1
  1. 1.Department of Information and Communication Technology, University of TrentoItaly

Personalised recommendations