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Risk management for self-adapting self-organizing emergent multi-agent systems performing dynamic task fulfillment

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Abstract

The goal of self-adapting self-organizing emergent multi-agent systems applied to problems with dynamically appearing tasks is to reduce operation and design costs. This is accomplished through the design of autonomous agents, which interact to produce behaviors required for flexible and scalable operation. However, when combined with agent autonomy, emergent behaviors are unpredictable resulting in a lack of trust for applications desiring efficiency such as logistics. An additional consultation agent, known as an efficiency improvement advisor (EIA), has been shown to increase efficiency through autonomy preserving advice provided as exception rule adaptations to agents. The problem addressed in this paper is that, in order for EIA-adapted systems to be deployed, the stakeholders must be assured that the risks of both autonomous and adapted behavior are properly assessed and managed. This paper presents a complete framework for a risk-aware EIA (RA-EIA) which uses reflection in order to manage the risks associated with autonomous agents and prospective adaptations. Monte Carlo simulation is used to reduce the frequency of emergent misbehavior appearing during regular operation. Meanwhile, an exploratory testing method, termed evolutionary learning of event sequences, is used to deal with the possibility of severe emergent misbehavior as the result of an malicious adversary or a series of unfortunate events. The experimental evaluations and accompanying descriptive example, for the application area of logistics via pickup and delivery problems, demonstrate that the risk-aware adaptations provided from consultation with the RA-EIA agent allow the client system to be trusted for long-term independent and reliable operational efficiency.

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References

  1. Akour, M., Jaidev, A., & King, T.M. (2011). Towards Change Propagating Test Models in Autonomic and Adaptive Systems. In Proceedings of the International Conference on Engineering of Computer-Based Systems, ECBS ’11 (pp. 89–96). IEEE Computer Society.

  2. Barbucha, D., & Jedrzejowicz, P. (2009). Agent-based approach to the dynamic vehicle routing problem. In Proceedings of the International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS’ 09 (pp. 169–178). Berlin: Springer.

  3. Berbeglia, G., Cordeau, J. F., Gribkovskaia, I., & Laporte, G. (2007). Static pickup and delivery problems: A classiffication scheme and survey. TOP, 15(1), 1–31.

    Article  MathSciNet  MATH  Google Scholar 

  4. Coelho, D. K., Roisenberg, M., de Freitas Filho, P. J., & Jacinto, C. M. C. (2005). Risk assessment of drilling and completion operations in petroleum wells using a monte carlo and a neural network approach. In Proceedings of the Winter Simulation Conference, WSC ’05 (pp. 1892–1897). Winter Simulation Conference.

  5. Davidsson, P., Persson, J. A., & Holmgren, J. (2007). On the integration of agent-based and mathematical optimization techniques. In Proceedings of the International Symposium on Agent and Multi-Agent Systems, KES-AMSTA ’07 (pp. 1–10). Berlin: Springer.

  6. De Wolf, T., & Holvoet, T. (2005). Emergence versus self-organisation: Different concepts but promising when combined. In S. A. Brückner, G. D. M. Serugendo, A. Karageorgos, & R. Nagpal (Eds.), Engineering Self-Organising Systems (Vol. 3464, pp. 1–15)., Lecture Notes in Computer Science Berlin: Springer.

    Chapter  Google Scholar 

  7. De Wolf, T., & Holvoet, T. (2007). A taxonomy for self-* properties in decentralised autonomic computing. In M. Parashar & S. Hariri (Eds.), Autonomic Computing: Concepts, Infrastructure, and Applications (pp. 101–120). Boca Raton: CRC Press.

    Google Scholar 

  8. Di Stefano, A., Pappalardo, G., Santoro, C., & Tramontana, E. (2007). A framework for the design and automated implementation of communication aspects in multi-agent systems. Journal of Network and Computer Applications, 30, 1136–1152.

    Article  Google Scholar 

  9. Dobson, S., Denazis, S., Fernández, A., Gaïti, D., Gelenbe, E., Massacci, F., et al. (2006). A survey of autonomic communications. ACM Transactions on Autonomous and Adaptive Systems, 1(2), 223–259.

    Article  Google Scholar 

  10. Dorer, K., & Calisti, M. (2005). An adaptive solution to dynamic transport optimization. In Proceedings of the International Joint Conference on Autonomous Agents and Multi-Agent Systems, AAMAS ’05 (pp. 45–51). ACM Press.

  11. Ferber, J., & Gutknecht, O. (1998). A meta-model for the analysis and design of organizations in multi-agent systems. In Proceedings of the International Conference on Multi Agent Systems, ICMAS ’98 (pp. 128–135). IEEE Computer Society.

  12. Flanagan, T., Thornton, C., & Denzinger, J. (2009). Testing harbour patrol and interception policies using particle-swarm-based learning of cooperative behavior. In Proceedings of the Computational Intelligence for Security and Defense Applications, CISDA ’09 (pp. 1–8).

  13. Hartigan, J. A. (1975). Clustering Algorithms. New York: Wiley.

    Google Scholar 

  14. Horn, P. (2001). Autonomic Computing: IBM’s Perspective on the State of Information Technology. Tech. rep., IBM.

  15. Huang, G., Liu, T., Hong, M., Zheng, Z., Liu, Z., & Fan, G. (2004). Towards Autonomic Computing Middleware via Reflection. Computer Software and Applications Conference, Annual International, 1, 135–140.

  16. Hudson, J. (2011). Risk assessment and management for efficient self-adapting self-organizing emergent multi-agent systemss. Master’s thesis, University of Calgary.

  17. Hudson, J., Denzinger, J., Kasinger, H., & Bauer, B. (2009). Testing Self-Organizing Emergent Systems by Learning of Event Sequences. Technical Report 2009–949-28, Department of Computer Science, University of Calgary.

  18. Hudson, J., Denzinger, J., Kasinger, H., & Bauer, B. (2010). Efficiency testing of self-adapting systems by learning of event sequences. In Proceedings of the International Conference on Adaptive and Self-adaptive Systems and Applications, ADAPTIVE ’10 (pp. 200–205).

  19. Hudson, J., Denzinger, J., Kasinger, H., & Bauer, B. (2011). Dependable risk-aware efficiency improvement for self-organizing emergent systems. In Proceedings of the International Conference on Self-Adaptive and Self-Organizing Systems, SASO ’11 (pp. 11–20).

  20. IBM (2006). Autonomic Computing Whitepaper: An Architectural Blueprint for Autonomic Computing. Technical Report.

  21. IBM (2008). The GMA 2008 logistics survey: Improving efficiency in the face of mounting logistics costs. http://www-935.ibm.com/services/us/gbs/bus/pdf/gbe03063-usen-gma08.

  22. Jain, R. (1991). The Art of Computer Systems Performance Analysis. New York: Wiley-Interscience.

    Google Scholar 

  23. Jelasity, M., Babaoglu, O., & Laddaga, R. (2006). Self-management through self-organization. IEEE Intelligent Systems, 21(2), 8–9.

    Article  Google Scholar 

  24. Kaner, C., Falk, J., & Nguyen, H. Q. (1993). Testing Computer Software. Boston: International Thomas Computer Press.

    MATH  Google Scholar 

  25. Kasinger, H. (2010). Design and Operation of Efficient Self-Organizing Systems. Ph.D. thesis, Universität Augsburg.

  26. Kasinger, H., Bauer, B., & Denzinger, J. (2008). The meaning of semiochemicals to the design of self-organizing systems. In Proceedings of the International Conference on Self-Adaptive and Self-Organizing Systems, SASO ’08 (pp. 139–148). IEEE Computer Society.

  27. Kasinger, H., Bauer, B., & Denzinger, J. (2009). Design pattern for self-organizing emergent systems based on digital infochemicals. In Proceedings of the International Conference and Workshops on Engineering of Autonomic and Autonomous Systems, EASe ’09 (pp. 45–55). IEEE Computer Society.

  28. Kasinger, H., Bauer, B., Denzinger, J., & Holvoet, T. (2010). Adapting environment-mediated self-organizing emergent systems by exception rules. In Proceedings of the International Workshop on Self-Organizing Architectures, SOAR ’10 (pp. 35–42). ACM Press.

  29. Kasinger, H., Denzinger, J., & Bauer, B. (2008). Digital semiochemical coordination. Communications of SIWN, 4, 133–139.

    Google Scholar 

  30. Kasinger, H., Denzinger, J., & Bauer, B. (2009). Decentralized coordination of homogeneous and heterogeneous agents by digital infochemicals. In Proceedings of the Symposium on Applied Computing, SAC ’09 (pp. 1223–1224). ACM Press.

  31. Kephart, J. O., & Chess, D. M. (2003). The vision of autonomic computing. IEEE Computer, 36(1), 41–50.

    Article  MATH  Google Scholar 

  32. King, T. M., Allen, A. A., Wu, Y., Clarke, P. J., & Ramirez, A. E. (2011). A comparative case study on the engineering of self-testable autonomic software. In Proceedings of the International Conference and Workshops on Engineering of Autonomic and Autonomous Systems, EASe ’11 (pp. 59–68). IEEE Computer Society.

  33. Maes, P., & Nardi, D. (Eds.). (1988). Meta-Level Architectures and Reflection. New York: Elsevier Science Inc.

    MATH  Google Scholar 

  34. Mahr, T., Srour, J., de Weerdt, M. M., & Zuidwijk, R. (2010). Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty. Transportation Research: Part C, 18(1), 99–119.

    Article  Google Scholar 

  35. McCabe, B. (2003). Monte Carlo simulation for schedule risks. In Proceedings of the Winter Simulation Conference, WSC ’03 (pp. 1561–1565). Winter Simulation Conference.

  36. Mogul, J. C. (2006). Emergent (mis)behavior vs. complex software systems. ACM SIGOPS Operating Systems Review, 40(4), 293–304.

    Article  Google Scholar 

  37. Nakrani, S., & Tovey, C. (2004). On honey bees and dynamic server allocation in internet hosting centers. Adaptive Behavior, 12, 223–240.

    Article  Google Scholar 

  38. Nguyen, C., Miles, S., Perini, A., Tonella, P., Harman, M., & Luck, M. (2012). Evolutionary testing of autonomous software agents. Autonomous Agents and Multi-Agent Systems, 25, 260–283.

    Article  Google Scholar 

  39. NIST/SEMATECH (2011). e-Handbook of Statistical Methods. http://www.itl.nist.gov/div898/handbook/.

  40. Ogata, K. (2009). Modern Control Engineering. Boston: Prentice Hall.

    Google Scholar 

  41. Pěchouček, M., Štepánková, O., Mařík, V., & Bárta, J. (2003). Abstract architecture for meta-reasoning in multi-agent systems. In Proceedings of the Central and Eastern European conference on Multi-agent systems, CEEMAS’03 (pp. 84–99). Springer-Verlag.

  42. Richter, U., Mnif, M., Branke, J., Müller-Schloer, C., & Schmeck, H. (2006). Towards a generic observer/controller architecture for Organic Computing. In Proceedings of Informatik, Informatik ’06 (pp. 112–119).

  43. Sailing, K. B., & Leleur, S. (2006). Assessment of transport infrastructure projects by the use of Monte Carlo simulation: The CBA-DK model. In Proceedings of the Winter Simulation Conference, WSC ’06 (pp. 1537–1544). Winter Simulation Conference.

  44. Sailing, K. B., & Leleur, S. (2007). Appraisal of airport alternatives in greenland by the use of risk analysis and monte carlo simulation. In Proceedings of the Winter Simulation Conference, WSC ’07 (pp. 1986–1993). Winter Simulation Conference.

  45. Schmitt, A., & Singh, M. (2009). Quantifying supply chain disruption risk using Monte Carlo and discrete-event simulation. In Proceedings of the Winter Simulation Conference, WSC ’09, (pp. 1237–1248). Winter Simulation Conference.

  46. Schumann, R., Lattner, A. D., & Timm, I. J. (2008). Management-by-exception—a modern approach to managing self-organizing systems. Communications of SIWN, 4, 168–172.

    Google Scholar 

  47. Serugendo, G. D. M., Gleizes, M. P., & Karageorgos, A. (2005). Self-organization in multi-agent systems. The Knowledge Engineering Review, 20(2), 165–189.

    Article  Google Scholar 

  48. Shaw, M. (1995). Beyond objects: A software design paradigm based on process control. ACM Software Engineering Notes, 20(1), 27–38.

    Article  Google Scholar 

  49. Steghöfer, J. P. (2008). Improving efficiency and coordination in self-organizing emergent systems. Master’s thesis, Universität Augsburg.

  50. Steghöfer, J. P., Denzinger, J., Kasinger, H., & Bauer, B. (2010). Improving the efficiency of self-organizing emergent systems by an advisor. In Proceedings of the International Conference and Workshops on Engineering of Autonomic and Autonomous Systems, EASe ’10 (pp. 63–72). IEEE Computer Society.

  51. Steiner, T. (2010). Improving Self-organizing Self-adapting Multi-Agent Systems using Predictive Exception Rules. Master’s thesis, Universität Augsburg.

  52. Steiner, T., Denzinger, J., Kasinger, H., & Bauer, B. (2011). Pro-active advice to improve the efficiency of self-organizing emergent system. In Proceedings of the International Conference and Workshops on Engineering of Autonomic and Autonomous Systems, EASe ’11 (pp. 97–106). IEEE Computer Society.

  53. Sterritt, R., Garrity, G., Hanna, E., & O’Hagan, P. (2006). Survivable security systems through autonomicity. In M. Hinchey, P. Rago, J. Rash, C. Rouff, R. Sterritt, & W. Truszkowski (Eds.), Innovative Concepts for Autonomic and Agent-Based Systems (Vol. 3825, pp. 379–389)., Lecture Notes in Computer Science Berlin: Springer.

    Chapter  Google Scholar 

  54. Toth, P., & Vigo, D. (2002). The Vehicle Routing Problem. Philadelphia, PA: Society for Industrial and Applied Mathematics.

  55. Truszkowski, W. F., Hinchey, M. G., Rash, J. L., & Rouff, C. A. (2006). Autonomous and autonomic systems: A paradigm for future space exploration missions. IEEE Transactions on Systems, Man, and Cybernetics—Part C, 36(3), 279–291.

    Article  Google Scholar 

  56. Weyns, D., Boucké, N., & Holvoet, T. (2008). A field-based versus a protocol-based approach for adaptive task assignment. Autonomous Agents and Multi-Agent Systems, 17(2), 288–319.

    Article  Google Scholar 

  57. Weyns, D., & Georgeff, M. (2010). Self-adaptation using multi-agent systems. IEEE Software, 27(1), 86–91.

    Article  Google Scholar 

  58. Weyns, D., Haesevoets, R., Van Eylen, B., Helleboogh, A., Holvoet, T., & Joosen, W. (2008). Endogenous versus exogenous self-management. In Proceedings of the International Workshop on Software Engineering for Adaptive and Self-managing Systems, SEAMS ’08 (pp. 41–48). ACM Press.

  59. Wooldridge, M. J., & Jennings, N. R. (1995). Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2), 115–152.

    Article  Google Scholar 

  60. Wu, D., Song, H., Li, M., Cai, C., & Li, J. (2010). Modeling risk factors dependence using Copula method for assessing software schedule risk. In Proceedings of the Software Engineering and Data Mining Conference, SEDM ’10 (pp. 571–574).

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  1. Department of Computer Science, University of Calgary, Calgary, Canada

    Jonathan Hudson & Jörg Denzinger

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Hudson, J., Denzinger, J. Risk management for self-adapting self-organizing emergent multi-agent systems performing dynamic task fulfillment. Auton Agent Multi-Agent Syst 29, 973–1022 (2015). https://doi.org/10.1007/s10458-014-9274-0

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