Conversational Case-Based Reasoning in Self-healing and Recovery

  • David McSherry
  • Sa’adah Hassan
  • David Bustard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5239)


Self-healing and recovery informed by environment knowledge (SHRIEK) is an autonomic computing approach to improving the robustness of computing systems. Case-based reasoning (CBR) is used to guide fault diagnosis and enable learning from experience, and rule-based reasoning to enable fault remediation and recovery informed by environment knowledge. Focusing on the role of conversational CBR (CCBR) in the management of faults that rely on user interaction for their detection and diagnosis, we present a hypothesis-driven approach to question selection in CCBR that aims to increase the transparency of CCBR dialogues by enabling the system to explain the relevance of any question the user is asked. We also present empirical results which suggest that there is no loss of problem-solving efficiency in the approach. Finally, we investigate the effects of the environment awareness provided by autonomous information gathering in SHRIEK on the efficiency of CCBR dialogues.


Autonomic computing self-healing environment awareness fault management case-based reasoning explanation transparency 


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  1. 1.
    Horn, P.: Autonomic Computing: IBM’s Perspective on the State of Information Technology. In: Agenda 2001. IBM Watson Research Center, Scottsdale (2001)Google Scholar
  2. 2.
    Hassan, S., McSherry, D., Bustard, D.: Autonomic Self Healing and Recovery Informed by Environment Knowledge. Artificial Intelligence Review 26, 89–101 (2006)CrossRefGoogle Scholar
  3. 3.
    Hassan, S., Bustard, D., McSherry, D.: Soft Systems Methodology in Autonomic Computing Analysis. In: UK Systems Society International Conference, pp. 106–115 (2006)Google Scholar
  4. 4.
    Checkland, P., Scholes, J.: Soft Systems Methodology in Action. Wiley, Chichester (1990)Google Scholar
  5. 5.
    Crapo, A.W., Aragones, A.V., Price, J.E., Varma, A.: Towards Autonomic Systems for Lifecycle Support of Complex Equipment. In: International Conference on Information Reuse and Integration, pp. 322–329. IEEE, Los Alamitos (2003)Google Scholar
  6. 6.
    Montani, S., Anglano, C.: Achieving Self-Healing in Service Delivery Software Systems by Means of Case-Based Reasoning. Applied Intelligence 28, 139–152 (2008)Google Scholar
  7. 7.
    Montani, S., Anglano, C.: Case-Based Reasoning for Autonomous Service Failure Diagnosis and Remediation in Software Systems. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 489–503. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Aha, D.W., Breslow, L.A., Muñoz-Avila, H.: Conversational Case-Based Reasoning. Applied Intelligence 14, 9–32 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Aha, D.W., McSherry, D., Yang, Q.: Advances in Conversational Case-Based Reasoning. Knowledge Engineering Review 20, 247–254 (2005)CrossRefGoogle Scholar
  10. 10.
    Gu, M., Aamodt, A.: Evaluating CBR Systems Using Different Data Sources: a Case Study. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 121–135. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    McSherry, D.: Interactive Case-Based Reasoning in Sequential Diagnosis. Applied Intelligence 14, 65–76 (2001)zbMATHCrossRefGoogle Scholar
  12. 12.
    Shimazu, H., Shibata, A., Nihei, K.: ExpertGuide: a Conversational Case-Based Reasoning Tool for Developing Mentors in Knowledge Spaces. Applied Intelligence 14, 33–48 (2001)zbMATHCrossRefGoogle Scholar
  13. 13.
    Cheetham, W.: A Mixed-Initiative Call Center Application for Appliance Diagnostics. In: AAAI 2005 Fall Symposium on Mixed-Initiative Problem-Solving Assistants. AAAI/MIT Press (2005)Google Scholar
  14. 14.
    McSherry, D.: Hypothetico-Deductive Case-Based Reasoning. In: ICCBR 2007 Workshop on Case-Based Reasoning in the Health Sciences, pp. 315–324 (2007)Google Scholar
  15. 15.
    McSherry, D.: Increasing Dialogue Efficiency in Case-Based Reasoning Without Loss of Solution Quality. In: 18th International Joint Conference on Artificial Intelligence, pp. 121–126 (2003)Google Scholar
  16. 16.
    McSherry, D.: Increasing the Coverage of Decision Trees through Mixed-Initiative Interaction. In: 18th Irish Conference on Artificial Intelligence and Cognitive Science, pp. 101–110 (2007)Google Scholar
  17. 17.
    Carrick, C., Yang, Q., Abi-Zeid, I., Lamontagne, L.: Activating CBR Systems through Autonomous Information Gathering. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS (LNAI), vol. 1650, pp. 74–88. Springer, Heidelberg (1999)Google Scholar
  18. 18.
    Giampapa, J., Sycara, K.: Conversational Case-Based Planning for Agent Team Coordination. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 189–203. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • David McSherry
    • 1
  • Sa’adah Hassan
    • 2
  • David Bustard
    • 1
  1. 1.School of Computing and Information EngineeringUniversity of UlsterColeraineNorthern Ireland
  2. 2.Faculty of Computer Science and Information TechnologyUniversity Putra Malaysia, 43400 UPM SerdangSelangorMalaysia

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