Applied Intelligence

, Volume 14, Issue 1, pp 77–94 | Cite as

A Case-Based Framework for Interactive Capture and Reuse of Design Knowledge

  • David B. Leake
  • David C. Wilson
Article

Abstract

Aerospace design is a complex task requiring access to large amounts of specialized information. Consequently, intelligent systems that support and amplify the abilities of human designers by capturing and presenting relevant information can profoundly affect the speed and reliability of design generation. This article describes research on supporting aerospace design by integrating a case-based design support framework with interactive tools for capturing expert design knowledge through “concept mapping.” In the integrated system, interactive concept mapping tools provide crucial functions for generating and examining design cases and navigating their hierarchical structure, while CBR techniques facilitate retrieval and aid interactive adaptation of designs. Our goal is both to provide a useful design aid and to develop general interactive techniques to facilitate case acquisition and adaptation. Experiments illuminate the performance of the system's context-sensitive retrieval during interactive case adaptation and the conditions under which it provides the most benefit.

case-based reasoning concept mapping interactive systems integrations knowledge acquisition design 

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References

  1. 1.
    A. Aamodt and E. Plaza, “Case-based reasoning: Foundational issues, methodological variations, and system approaches,” AI Communications, vol. 7, no. 1, pp. 39–52, 1994.Google Scholar
  2. 2.
    J. Kolodner, Case-Based Reasoning, Morgan Kaufmann: San Mateo, CA, 1993.Google Scholar
  3. 3.
    D. Leake (Ed.), Case-Based Reasoning: Experiences, Lessons, and Future Directions, AAAI Press: Menlo Park, CA, 1996.Google Scholar
  4. 4.
    C. Riesbeck and R. Schank, Inside Case-Based Reasoning, Lawrence Erlbaum: Hillsdale, NJ, 1989.Google Scholar
  5. 5.
    I.Watson, Applying Case-Based Reasoning: Techniques for Enterprise Systems, Morgan Kaufmann: San Mateo, CA, 1997.Google Scholar
  6. 6.
    H. Kitano and H. Shimazu, “The experience sharing architecture: Acase study in corporate-wide case-based software quality control,” in Case-Based Reasoning: Experiences, Lessons, and Future Directions, edited by D. Leake, AAAI Press: Menlo Park, CA, pp. 235–268, 1996.Google Scholar
  7. 7.
    J. Novak and D. Gowin, Learning How to Learn, Cambridge University Press: New York, 1984.Google Scholar
  8. 8.
    A. Cañas, D. Leake, and D. Wilson, “Managing, mapping, and manipulating conceptual knowledge,” in Proceedings of the AAAI-99 Workshop on Exploring Synergies of Knowledge Management and Case-Based Reasoning, AAAI Press: Menlo Park, 1999, pp. 10–14.Google Scholar
  9. 9.
    T. Bagg, RECALL: Reusable experience with case-based reasoning for automating lessons learned, NASA, Moffet Field, CA, 1997 (http://hope.gsfc. nasa.gov/RECALL/homepg/recall.htm)Google Scholar
  10. 10.
    W. Wilke, I. Vollrath, K.-D. Althoff, and R. Bergmann, “A framework for learning adaptation knowledge based on knowledge light approaches,” in Proceedings of the Fifth German Workshop on Case-Based Reasoning, 1997, pp. 235–242.Google Scholar
  11. 11.
    J. Kolodner, “Improving human decision making through casebased decision aiding,” The AI Magazine, vol. 12, no. 2, pp. 52–68, 1991.Google Scholar
  12. 12.
    M. Lenz, B. Bartsch-Spörl, H.-D. Burkhard, and S.Wess (Eds.), Case-Based Reasoning Technology: From Foundations to Applications, Springer: Berlin, 1998.Google Scholar
  13. 13.
    E. Domeshek, M. Herndon, A. Bennett, and J. Kolodner, “A case based design aid for conceptual design of aircraft subsystems,” in Proceedings of the Tenth ieee Conference on Artificial Intelligence for Applications, IEEE Computer Society Press: Washington, 1994, pp. 63–69.Google Scholar
  14. 14.
    F. Gebhardt, A. Voß, W. Gräther, and B. Schmidt-Belz, Reasoning with Complex Cases, Kluwer: Boston, 1997.Google Scholar
  15. 15.
    A. Goel, J. Kolodner, M. Pearce, and R. Billington, “Towards a case-based tool for aiding conceptual design problem solving,” in Proceedings of theDARPACase-Based Reasoning Workshop, edited by R. Bareiss, Morgan Kaufmann: San Mateo, 1991, pp. 109–120.Google Scholar
  16. 16.
    K. Hua and B. Faltings, “Exploring case-based design-CADRE,” Artificial Intelligence in Engineering Design, Analysis and Manufacturing, vol. 7, no. 2, pp. 135–144, 1993.Google Scholar
  17. 17.
    A.G. de Silva Garza and M. Maher, “Design by interactive exploration using memory-based techniques,” Knowledge-Based Systems, vol. 9, no. 1, 1996.Google Scholar
  18. 18.
    I. Smith, C. Lottaz, and B. Faltings, “Spatial composition using cases: IDIOM, in Proceedings of First International Conference on Case-Based Reasoning, Springer Verlag: Berlin, 1995, pp. 88–97.Google Scholar
  19. 19.
    I. Vollrath, “Reuse of complex electronic designs: Requirements analysis for a CBR application,” in Proceedings of the Fourth European Workshop on Case-Based Reasoning, edited by P. Cunningham, B. Smyth, and M. Keane, Springer Verlag: Berlin, 1998, pp. 136–147.Google Scholar
  20. 20.
    K. Sycara, R. Guttal, J. Koning, S. Narasimhan, and D. Navinchandra, “CADET: A case-based synthesis tool for engineering design,” International Journal of Expert Systems, vol. 4, no. 2, pp. 157–188, 1991.Google Scholar
  21. 21.
    A. Cañas, K. Ford, J. Brennan, T. Reichherzer, and P. Hayes, “Knowledge construction and sharing in quorum,” in World conference on artificial intelligence in education, Canas, Norfolk VA, pp. 218–225, 1995.Google Scholar
  22. 22.
    D. Jonassen, K. Beissner, and M. Yacci, “Explicit methods for conveying structural knowledge through concept maps,” in Structural Knowledge: Techniques for Representing, Conveying, and Acquiring Structural Knowledge, Erlbaum: Hillsdale, NJ, p. 155, 1993.Google Scholar
  23. 23.
    G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Information Processing and Management, vol. 24, no. 5, pp. 513–523, 1988.Google Scholar
  24. 24.
    D. Leake, A. Kinley, and D. Wilson, “A case study of case-based CBR,” in Proceedings of the Second International Conference on Case-Based Reasoning, Springer Verlag: Berlin, 1997a, pp. 371–382.Google Scholar
  25. 25.
    K. Sycara, “Resolving adversarial conflicts: An approach integrating case-based and analytic methods,” Unpublished doctoral dissertion, School of Information and Computer Science, Georgia Institute of Technology. (Georgia Institute of Technology, Technical Report GIT-ICS-87/26), 1987.Google Scholar
  26. 26.
    K. Hammond, Case-Based Planning: Viewing Planning as a Memory Task, Academic Press: San Diego, 1989.Google Scholar
  27. 27.
    L. Ihrig and S. Kambhampati, “Storing and indexing plan derivations through explanation-based analysis of retrieval failures,” Journal of Artificial Intelligence Research, vol. 7, pp. 161–198, 1997.Google Scholar
  28. 28.
    R. Schank, Dynamic Memory: A Theory of Learning in Computers and People, Cambridge University Press: Cambridge, England, 1982.Google Scholar
  29. 29.
    D.W. Aha and L. Breslow, “Refining conversational case libraries,” in Proceedings of the Second International Conference on Case-Based Reasoning, Springer Verlag: Berlin, 1997, pp. 267–278.Google Scholar
  30. 30.
    S. Fox and D. Leake, “Modeling case-based planning for repairing reasoning failures,” in Proceedings of the 1995 aaai Spring Symposium on Representing Mental States and Mechanisms, AAAI Press: Menlo Park, CA, 1995, pp. 31–38.Google Scholar
  31. 31.
    Z. Zhang and Q. Yang, “Towards life-time maintenance of case base indexes for continual case based reasoning,” in Proceedings of the 1998 International Conference on ai Methodologies, Systems and Applications (AIMSA-98), Springer Verlag: Berlin, 1998, pp. 489–500.Google Scholar
  32. 32.
    D. Leake, L. Birnbaum, K. Hammond, C. Marlow, and H. Yang, “Integrating information resources: A case study of engineering design support,” in Proceedings of the Third International Conference on Case-Based Reasoning, Springer Verlag: Berlin, 1999, pp. 482–496.Google Scholar
  33. 33.
    W. Mark, E. Simoudis, and D. Hinkle, “Case-based reasoning: Expectations and results,” in Case-Based Reasoning: Experiences, Lessons, and Future Directions, edited by D. Leake, AAAI Press: Menlo Park, CA, 1996.Google Scholar
  34. 34.
    E. Simoudis, K. Ford, and A. Cañas, “Knowledge acquisition in case-based reasoning: “... and then a miracle happens,” in Proceedings of the 1992 Florida AI Research Symposium, edited by D. Dankel, Florida Artificial Intelligence Research Society, 1992.Google Scholar
  35. 35.
    A. Voß, “The need for knowledge aquisition in case-based reasoning-some experiences from an architectural domain,” in Proceedings of the Eleventh European Conference on Artificial Intelligence, John Wiley, 1994, pp. 463–467.Google Scholar
  36. 36.
    M. Lenz and K. Ashley (Eds.), in Proceedings of the AAAI-98 Workshop on Textual Case-Based Reasoning, AAAI Press: Menlo Park, CA, 1998.Google Scholar
  37. 37.
    D. Leake and D. Wilson, “Case-base maintenance: Dimensions and directions,” in Proceedings of the Fourth European Workshop on Case-Based Reasoning, edited by P. Cunningham, B. Smyth, and M. Keane, Springer Verlag: Berlin, 1998, pp. 196–207.Google Scholar
  38. 38.
    T. Gruber and D. Russell, Generative Design Rationale: Beyond the Record and Replay Paradigm (Knowledge Systems Laboratory KSL 92-59). Computer Science Department, Stanford University, 1992.Google Scholar
  39. 39.
    R. Bergmann, H. Muñoz-Avila, M. Veloso, and E. Melis, “Case-based reasoning applied to planning tasks,” in CBR Technology: From Foundations to Applications, edited by M. Lenz, B. Bartsch-Spörl, H.-D. Burkhard, and S. Wess, Springer: Berlin, pp. 169–199, 1998.Google Scholar
  40. 40.
    J. Carbonell, “Derivational analogy: A theory of reconstructive problem solving and expertise acquisition,” in Machine Learning: An Artificial Intelligence Approach, edited by R. Michalski, J. Carbonell, and T. Mitchell, Morgan Kaufmann: Los Altos, CA, vol. 2, pp. 371–392, 1986.Google Scholar
  41. 41.
    M. Veloso, Planning and Learning by Analogical Reasoning, Springer Verlag: Berlin, 1994.Google Scholar
  42. 42.
    M. Veloso, A. Mulvehill, and M. Cox, “Rationale-supported mixed-initiative case-based planning,” in Proceedings of the Ninth Conference on Innovative Applications of Artificial Intelligence, AAAI Press: Menlo Park, CA, 1997, pp. 1072–1077.Google Scholar
  43. 43.
    D. Leake, A. Kinley, and D.Wilson, “Case-based CBR: Capturing and reusing reasoning about case adaptation,” International Journal of Expert Systems, vol. 10, no. 2, pp. 197–213, 1997b.Google Scholar
  44. 44.
    P. Clark, “A model of argumentation and its application in a cooperative expert system,” Strathclyde University, Glascow, UK, 1991.Google Scholar
  45. 45.
    K. Hammond, R. Burke, and K. Schmitt, “A case-based approach to knowledge navigation,” in Case-Based Reasoning: Experiences, Lessons, and Future Directions, edited by D. Leake, AAAI Press: Menlo Park, CA.Google Scholar
  46. 46.
    M. Jaczynski and B. Trousse, “WWW assisted browsing by reusing past navigations of a group of users,” in Proceedings of the Fourth European Workshop on Case-Based Reasoning, edited by P. Cunningham, B. Smyth, and M. Keane, Springer Verlag: Berlin, 1998, pp. 160–171.Google Scholar
  47. 47.
    M. Fagan and S. Corley, “CBR for the reuse of corporate SQL knowledge,” in Proceedings of the Fourth European Workshop on Case-Based Reasoning, edited by P. Cunningham, B. Smyth, and M. Keane, Springer Verlag: Berlin, 1998, pp. 382–391.Google Scholar
  48. 48.
    D. Leake, “Towards a computer model of memory search strategy learning,” in Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, Lawrence Erlbaum: Hillsdale, NJ, 1994, pp. 549–554.Google Scholar
  49. 49.
    D. Leake, A. Kinley, and D. Wilson, “Learning to improve case adaptation by introspective reasoning and CBR,” in Proceedings of the First International Conference on Case-Based Reasoning, Springer Verlag: Berlin, 1995, pp. 229–240.Google Scholar
  50. 50.
    M. Göker, T. Roth-Berghofer, R. Bergman, T. Pantleon, R. Traphöner, S. Wess, and W. Wilke, “The development of HOMER: A case-based CAD/CAM help-desk support tool,” in Proceedings of the Fourth European Workshop on Case-Based Reasoning, edited by P. Cunningham, B. Smyth, and M. Keane, Springer Verlag: Berlin, 1998, pp. 346–357.Google Scholar
  51. 51.
    W. Ferguson, R. Bareiss, R. Osgood, and L. Birnbaum, “ASK systems: An approach to the realization of story-based teachers,” The Journal of the Learning Sciences, vol. 1, pp. 95–134, 1992.Google Scholar
  52. 52.
    D. Leake and D. Wilson, “Combining CBR with interactive knowledge acquisition, manipulation and reuse,” in Proceedings of the Third International Conference on Case-Based Reasoning, Springer Verlag: Berlin, 1999, pp. 203–217.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • David B. Leake
    • 1
  • David C. Wilson
    • 1
  1. 1.Computer Science Department, Lindley Hall 215Indiana UniversityBloomingtonUSA

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