Skip to main content

Systems, tasks and adaptation knowledge: Revealing some revealing dependencies

  • Poster Sessions
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1010))

Abstract

This paper shows that the use of adaptation knowledge in CBR systems is heavily dependent on certain task and system constraints. Furthermore, the type of adaptation knowledge used in systems performing specific tasks is quite regular and predictable. These conclusions are reached by reviewing forty-two CBR systems and classifying them according to three taxonomies: an adaptation-relevant taxonomy of CBR systems, a taxonomy of tasks and a taxonomy of adaptation knowledge. We then show how different systems cluster with respect to interactions between these three taxonomies. The CBR system designer may find the partition of CBR systems and the division of adaptation knowledge suggested by this paper useful. Moreover, this paper may help focus the initial stages of systems development by suggesting (on the basis of existing work) what types of adaptation knowledge should be supported by a new system. In addition, the paper provides a framework for the preliminary evaluation and comparision of systems.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aamodt A., Plaza E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and Systems Approaches. AI Communications. 7(1) (1994) 39–59

    Google Scholar 

  2. Alterman R.: Adaptive Planning. Cognitive Science 12 (1988) 393–422

    Google Scholar 

  3. Ashley K.: Reasoning with Cases and Hypotheticals in HYPO. International Journal Man-Machine Studies 34 (1991) 753–796

    Google Scholar 

  4. Bain W.: JUDGE.: In Riesbeck C., Schank R. (Ed.) Inside Case-Based Reasoning. Northvale, NJ: Erlbaum (1989)

    Google Scholar 

  5. Bareiss E.: Exemplar-Based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning. Boston: Academic Press (1989)

    Google Scholar 

  6. Bareiss E., Slator B.: The Evolution of a Case-based Computational Approach to Knowledge Representation, Classification, and Learning. In Nakumura G., Medin D., Taraban R. (Ed.) Categorisation by Humans and Machines. New York: Academic Press (1993)

    Google Scholar 

  7. Berger J.: Roentgen: Radiation Theraphy and Case-Based Reasoning. In Proceedings of the 10th Conference on Artificial Intelligence for Applications. IEEE Computer Society Press (1994)

    Google Scholar 

  8. Bhansali S., Harandi M.: Syntesis of UNIX Programs Using Derivational Analogy, Machine Learning 10 (1993) 7–55.

    Google Scholar 

  9. Branting L.: Exploiting the Complementarity of Rules and Precedents with Reciprocity and Fairness. In Bareiss E. (Ed.) Proceedings: Case-Based Reasoning Workshop (1991) 39–50.

    Google Scholar 

  10. Carbonell J.: Learning by Analogy: Formulating and Generalizing Plans from Past Experience. In Michalski R., Carbonell J., Mitchell T. (Ed.) Machine Learning: An Artificial Intelligence Approach Vol. 1. Morgan Kaufmann (1983)

    Google Scholar 

  11. Carbonell J.: Derivational Analogy: A Theory of Reconstructive Problem Solving and Expertise Acquisition. In Michalski R., Carbonell J. Mitchell T. (Ed.) Machine Learning: An Artificial Intelligence Approach Vol. 2. Morgan Kaufmann (1986)

    Google Scholar 

  12. Clancey W.: Heuristic Classification., Artificial Intelligence 27(3) (1985) 289–350.

    Google Scholar 

  13. Collins G.: Plan Creation. In Riesbeck C., Schank R. (Ed.) Inside Case-based Reasoning. Northvale, NJ: Erlbaum (1989)

    Google Scholar 

  14. Cunningham P., Smyth B., Veale T.: On the Limitations of Memory Based Reasoning In Keane M.T., Haton J-P., Manago M. (Ed.) Proceedings Second European Workshop on Case-Based Reasoning. (1994) 59–65

    Google Scholar 

  15. Cunningham P., Smyth B., Bonzano A.: An Incremental Case Retrieval Mechanism for Diagnosis. Technical Report TCD-CS-95-01. Department of Computer, Science Trinity College Dublin (1995)

    Google Scholar 

  16. Dave B., Schmitt G., Shen-Guan S., Bendel L., Faltings B., Smith I., Hua K., Bailey S., Ducret J-M, Jent K.: Case-Based Spatial Design Reasoning. In Keane M.T., Haton J-P., Manago M. (Ed.) Proceedings of the Second European Workshop on Case-Based Reasoning (1994) 115–123

    Google Scholar 

  17. Domeshek E., Kolodner J.: Using the Points of Large Cases AI EDAM 7(2) (1993) 87–96

    Google Scholar 

  18. Ferguson W., Bareiss R., Birbaum L., Osgood R.: ASK Systems: An Approach to the Realization of Story-Based Teachers. The Journal of the Learning Sciences 2(1) (1992) 95–134

    Google Scholar 

  19. Goel A. Integration of Case-Based Reasoning and Model-Based Reasoning for Adaptive Design Problem Solving. PhD Dissertation, Department of Computer and Information Science, The Ohio State University (1989)

    Google Scholar 

  20. Goel A., Callantine T.:An Experience-Based Approach to Navigational Route Planning. In Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems (1992) 705–710.

    Google Scholar 

  21. Hammond K.: Case-Based Planning: Viewing Planning as a Memory Task. Boston: Academic Press (1989)

    Google Scholar 

  22. Hinkle D., Toomey C.: Clavier: Applying Case-Based Reasoning to Composite Part Fabrication. In Proceedings of the Sixth Innovative Applications of Artificial Intelligence Conference (1994) 55–61

    Google Scholar 

  23. Hinrichs T.: Problem Solving in Open Worlds: A case study in design. Northvale, NJ:Erlbaum (1992)

    Google Scholar 

  24. Kambhampati S. Hendler J.: Validation-structure-based Theory of Plan Modification and Reuse. Artificial Intelligence 55 (1992) 193–258

    Google Scholar 

  25. Kolodner J.: Case-based Reasoning Morgan Kaufmann (1993)

    Google Scholar 

  26. Koton P.: Using Experience in Learning and Problem Solving. PhD Dissertation Department of Computer Science, MIT. (1989)

    Google Scholar 

  27. Lekkas G., Avouris N.: Case-Based Reasoning in Environmental Monitoring. Applied Artificial intelligence 8 (1994) 359–376.

    Google Scholar 

  28. Lopez B., Plaza E.: Case-based Planning for Medical Diagnosis. In Komorowski J., Ras Z.W. (Ed.) Methodologies for Intelligent Systems. Lecture notes in artificial intelligence 689 (1993)

    Google Scholar 

  29. Maher M., Zhang D.: CADSYN: A Case-Based Design Process Model. AI-EDAM 7 (2) (1993) 97–110

    Google Scholar 

  30. Mostow J.: Design by Derivational Analogy. Artificial Intelligence 40 (1989) 119–184

    Google Scholar 

  31. Navinchandra D.: Exploration and Innovation in Design, Towards a Computational Model. New York Springer-Verlag (1991)

    Google Scholar 

  32. Price C., Pegler I.S., Bell F.: Case-Based Reasoning in the Melting Pot. International Journal of Applied Expert Systems 1(2) (1993) 120–133.

    Google Scholar 

  33. Pu P., Reschberger M. Assembly Sequence Planning using Case-Based Reasoning Techniques. In Gero J. (Ed.) Artificial Intelligence in Design Boston: Kluwer Academic Publishers (1991)

    Google Scholar 

  34. Ram A., Arkin R., Moorman K., Clark R.: Case-based Reactive Navigation: A case-based Method for On-line Selection and Adaptation of Reactive Control Parameters in Autonomous Robotic Systems, Technical Report GIT-CC-92/57, School of Information and Computer Science, Georgia Institute of Technology (1992)

    Google Scholar 

  35. Redmond M.: Learning by Observing and Understanding Expert Problem Solving. PhD Dissertation, School of Information and Computer Science, Georgia Institute of Technology (1992)

    Google Scholar 

  36. Riesbeck C., Schank R.: Inside Case-based Reasoning Lawrence Erlbaum Associates (1992)

    Google Scholar 

  37. Roderman S., Tsatsoulis C.: PANDA: A Case-Based System to Aid Novice Designers. AI EDAM 7(2) (1993) 125–133.

    Google Scholar 

  38. Rougegrez-Loriette S.: Prédiction de Processus à partir de Comportements observés: Le système REBECAS. Thèse du Doctorat de l'Université Paris 6 (1994)

    Google Scholar 

  39. Schank R., Kass A., Riesbeck C.: Inside Case-based Explanation. Lawrence Erlbaum Associates, Hillsdale, New Jersey (1994)

    Google Scholar 

  40. Schaal S., Atkeson C.: Robot Juggling: Implementation of Memory-Based Learning. IEEE Control Systems 14(1) 57–71

    Google Scholar 

  41. Simoudis E.: Using Case-Based Reasoning for Customer Technical Support. IEEE Expert 7(5) (1992) 7–13

    Google Scholar 

  42. Simpson R.: A Computer Model of Case-Based Reasoning in Problem Solving: An Investigation in the Domain of Dispute Mediation, Technical Report GIT-ICS-85/18, School of Information and Computer Science, Georgia Institute of Technology (1985)

    Google Scholar 

  43. Skalak D., Rissland E.: Arguments and Cases: An Inevitable Intertwining. Artificial Intelligence and Law 1 (1992) 3–44

    Google Scholar 

  44. Slattery S.: Case-based Reasoning. The derivational analogy approach. B.A. Project, Computer Science Department. Trinity College Dublin (1993)

    Google Scholar 

  45. Smyth B., Cunningham P.: Deja Vu: A Hierarchical Case-Based Reasoning System for Software Design. In Proceedings of the 10th European Conference on Artificial Intelligence. Vienna, Austria (1992)

    Google Scholar 

  46. Stanfill C., Waltz D.: Toward Memory-Based Reasoning. Communications of the ACM 29(2) (1986) 1213–1228

    Google Scholar 

  47. Sycara E. P.: Resolving Adversarial Conflicts: An Approach to Integrating Case-Based and Analytic Methods. PhD Dissertation,.School of Information and Computer Science, Georgia Institute of Technology (1987)

    Google Scholar 

  48. Sycara E. P., Navinchandra D.: Influences: A Thematic Abstraction for Creative Use of Multiple Cases. In Bareiss E.R. (Ed.) Proceedings: Case-Based Reasoning Workshop (1991)

    Google Scholar 

  49. Tsatsoulis C., Kashyap R.: Case-Based Reasoning and Learning in Manufacturing with the TOLTEC Planner. IEEE Transactions on Systems, Man and Cybernetics 23(4) (1993) 1010–1022

    Google Scholar 

  50. Veloso M.: Learning by Analogical Reasoning in General Problem Solving, PhD Thesis. School of Computer Science. Carnegie Mellon University, Pittsburgh, PA. (1992)

    Google Scholar 

  51. Wang J., Howard H.: A design-dependent approach to Integrated Structural Design. In Gero J. (Ed) Artificial Intelligence in Design. Boston: Kluwer Academic Publishers (1991)

    Google Scholar 

  52. Watson I., Marir F.: Case-based Reasoning: A Review. The Knowledge Engineering Review 9(4) (1994): 1–39

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Manuela Veloso Agnar Aamodt

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hanney, K., Keane, M., Smyth, B., Cunningham, P. (1995). Systems, tasks and adaptation knowledge: Revealing some revealing dependencies. In: Veloso, M., Aamodt, A. (eds) Case-Based Reasoning Research and Development. ICCBR 1995. Lecture Notes in Computer Science, vol 1010. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60598-3_42

Download citation

  • DOI: https://doi.org/10.1007/3-540-60598-3_42

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60598-0

  • Online ISBN: 978-3-540-48446-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics