Advertisement

The Fun Begins with Retrieval: Explanation and CBR

  • Edwina L. Rissland
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)

Abstract

This paper discusses the importance of the post-retrieval steps of CBR, that is, the steps that occur after relevant cases have been retrieved. Explanations and arguments, for instance, require much to be done post-retrieval. I also discuss both the importance of explanation to CBR and the use of CBR to generate explanations.

Keywords

Legal Reasoning Legal Argument Statutory Interpretation Business School Student Intelligent Learning Environment 
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.
    Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Artificial Intelligence Communications 7(1), 39–59 (1994)Google Scholar
  2. 2.
    Aleven, V.: Using background knowledge in case-based legal reasoning: A computational model and an intelligent learning environment. Artificial Intelligence 150(1-2), 183–237 (2003)MATHCrossRefGoogle Scholar
  3. 3.
    Aleven, V., Ashley, K.D.: Automated Generation of Examples for a Tutorial in Case Based Argumentation. In: Second International Conference on Intelligent Tutoring Systems (ITS 1992), pp. 575–584 (1992)Google Scholar
  4. 4.
    Ashley, K.D.: Modeling Legal Argument: Reasoning with Cases and Hypotheticals. MIT Press, Cambridge (1990)Google Scholar
  5. 5.
    Branting, L.K.: Building Explanations from rules and structured cases. International Journal of Man-Machine Studies 34(6), 797–837 (1991)CrossRefGoogle Scholar
  6. 6.
    Branting, L.K.: Reasoning with Rules and Precedents. Kluwer Academic Publishers, Dordrecht (2000)Google Scholar
  7. 7.
    Bridge, D.: The Virtue of Reward: Performance, Reinforcement, and Discovery in Case-Based Reasoning. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Cheetham, W.: Tenth Anniversary of the Plastics Color Formulation Tool. AI Magazine 26(3), 51–61 (2005)Google Scholar
  9. 9.
    Daniels, J.J., Rissland, E.L.: What You Saw Is What You Want: Using Cases to Seed Information Retrieval. In: Leake, D.B., Plaza, E. (eds.) ICCBR 1997. LNCS, vol. 1266, pp. 325–337. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  10. 10.
    Falkenhainer, B., Forbus, K.D., Gentner, D.: The structure-mapping engine: algorithm and examples. Artificial Intelligence 41(1), 1–63 (1989)MATHCrossRefGoogle Scholar
  11. 11.
    Forbus, K., Gentner, D., Law, K.: MAC/FAC: A model of similarity-based retrieval. Cognitive Science 19, 141–205 (1994)CrossRefGoogle Scholar
  12. 12.
    Gardner, A.: vdL. An Artificial Intelligence Approach to Legal Reasoning. MIT Press, Cambridge (1987)Google Scholar
  13. 13.
    Gentner, D.: Structure-mapping: A theoretical framework for analogy. Cognitive Science 7(2), 155–170 (1983)CrossRefGoogle Scholar
  14. 14.
    Göker, M., Roth-Berghofer, T.: Development and utilization of a Case-Based Help desk Support Sytem in a Corporate Environment. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS, vol. 1650, pp. 132–146. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  15. 15.
    Golding, A.R., Rosenbloom, P.S.: Improving rule-based systems through case-based reasoning. In: Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI 1991), pp. 22–27 (1991)Google Scholar
  16. 16.
    Hammond, K.J.: Case-based planning. Academic Press, London (1989)Google Scholar
  17. 17.
    Hastings, J., Branting, L.K., Lockwood, J.: CARMA: A case-based rangeland management advisor. AI Magazine 23(2), 49–62 (2002)Google Scholar
  18. 18.
    Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  19. 19.
    Koton, P.: Reasoning about evidence in causal explanation. In: Proceedings Seventh National on Artificial Intelligence (AAAI 1988), pp. 256–261 (1988)Google Scholar
  20. 20.
    Kotovsky, L., Gentner, D.: Comparison and categorization in the development of relational similarity. Child Development 67, 2797–2822 (1986)CrossRefGoogle Scholar
  21. 21.
    Lakatos, I.: Proofs and Refutations: The Logic of Mathematical Discovery. Cambridge University Press, Cambridge (1976)MATHGoogle Scholar
  22. 22.
    Leake, D.B.: Evaluating Explanations: A Content Theory. Lawrence Erlbaum, Mahwah (1992)Google Scholar
  23. 23.
    Leake, D.B. (ed.): Case-Based Reasoning: Experiences, Lessons, & Future Directions. AAAI Press/MIT Press (1996)Google Scholar
  24. 24.
    Leake, D.B., McSherry, D. (eds.): Artificial Intelligence Review: Special Issue on Case-Based Reasoning and Explanation, vol. 24(2) (2005)Google Scholar
  25. 25.
    Loewenstein, J., Thompson, L., Gentner, D.: Analogical encoding facilitates knowledge transfer in negotiation. Psychonomic Bulletin & Review 6, 586–597 (1999)CrossRefGoogle Scholar
  26. 26.
    Marling, C.R., Petot, G.J., Sterling, L.S.: Integrating case-based and rule-based reasoning to meet multiple design constraints. Computational Intelligence 15(3), 308–332 (1999)CrossRefGoogle Scholar
  27. 27.
    Marling, C., Muñoz-Avila, H., Rissland, E.L., Sqalli, M., Aha, D.: Case-Based Reasoning Integrations. AI Magazine 23(1), 69–86 (Spring 2002)Google Scholar
  28. 28.
    Marling, C., Rissland, E.L., Aamodt, A.: Integrations with case-based reasoning. Knowledge Engineering Review; Special issue on Case-Based Reasoning 20(3) (2006)Google Scholar
  29. 29.
    McCarty, L.T., Sridharan, N.S.: The Representation of an Evolving System of Legal Concepts: I. Logical Templates. In: Proceedings Third National Conference of the Canadian Society for Computational Studies of Intelligence, Victoria, May 1980, pp. 304–311 (1980)Google Scholar
  30. 30.
    McLaren, B.M.: Extensionally defining principles and cases in ethics: An AI model. Artificial Intelligence 150(1-2), 145–181 (2003)MATHCrossRefGoogle Scholar
  31. 31.
    McSherry, D.: Explanation in Recommender Systems. In: Leake, D.B., McSherry, D. (eds.) Artificial Intelligence Review: Special Issue on Case-Based Reasoning and Explanation, vol. 24(2) (2005)Google Scholar
  32. 32.
    Medin, D.: Concepts and conceptual structure. American Psychologist 44, 1469–1481 (1989)CrossRefGoogle Scholar
  33. 33.
    Medin, D.L., Shaffer, M.M.: Context theory of classification learning. Psychological Review 85, 207–238 (1978)CrossRefGoogle Scholar
  34. 34.
    Murphy, G.L.: The Big Book of Concepts. MIT Press, Cambridge (2002)Google Scholar
  35. 35.
    Namy, L.L., Gentner, D.: Making a silk purse out of two sows’ ears: Young children’s use of comparison in category learning. Journal of Experimental Psychology: General 131, 5–15 (2002)CrossRefGoogle Scholar
  36. 36.
    Polya, G.: Mathematical Discovery, vol. 2. John Wiley & Sons, Chichester (1965)MATHGoogle Scholar
  37. 37.
    Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Explaining Compound Critiques. In: Leake, D.B., McSherry, D. (eds.) Artificial Intelligence Review: Special Issue on Case-Based Reasoning and Explanation, vol. 24(2) (2005)Google Scholar
  38. 38.
    Rissland, E.(M.): Understanding Understanding Mathematics. Cognitive 2(4), 361–383 (1978)CrossRefGoogle Scholar
  39. 39.
    Rissland, E.L.: Example Generation. In: Proceedings Third National Conference of the Canadian Society for Computational Studies of Intelligence, Victoria, May 1980, pp. 280–288 (1980)Google Scholar
  40. 40.
    Rissland, E.L.: Examples in Legal Reasoning: Legal Hypotheticals. In: Proceedings Eighth International Joint Conference on Artificial Intelligence (IJCAI 1983), August 1983, pp. 90–93 (1983)Google Scholar
  41. 41.
    Rissland, E.L.: The Ubiquitous Dialectic. In: Proceedings Sixth European Conference on Artificial Intelligence (ECAI 1984), Pisa, pp. 367–372. North-Holland, Amsterdam (1984)Google Scholar
  42. 42.
    Rissland, E.L., Ashley, K.D.: Hypotheticals as Heuristic Device. In: Proceedings Fifth National Conference on Artificial Intelligence (AAAI 1986), Philadelphia, pp. 289–297 (1986)Google Scholar
  43. 43.
    Rissland, E.L., Ashley, K.D., Loui., R.P.: AI and Law: A Fruitful Synergy. Artificial Intelligence 150(1-2), 1–15 (2003)CrossRefMathSciNetGoogle Scholar
  44. 44.
    Rissland, E.L., Daniels, J.J.: Using CBR to Drive IR. In: Proceedings Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 1995), Montreal, pp. 400–407 (1995)Google Scholar
  45. 45.
    Rissland, E.L., Daniels, J.J.: The Synergistic Application of CBR to IR. Artificial Intelligence Review: Special Issue on the use of AI in Information Retrieval 10, 441–475 (1996)Google Scholar
  46. 46.
    Rissland, E.L., Skalak, D.B.: CABARET: Statutory Interpretation in a Hybrid Architecture. International Journal of Man-Machine Studies 34(6), 839–887 (1991)CrossRefGoogle Scholar
  47. 47.
    Rissland, E.L., Soloway, E.M.: Overview of an Example Generation System. In: Proceedings First National Conference on Artificial Intelligence (AAAI 1980), Stanford, pp. 256–258 (1980)Google Scholar
  48. 48.
    Rissland, E.L., Valcarce, E.M., Ashley, K.D.: Explaining and Arguing with Examples. In: Proceedings Fourth National on Artificial Intelligence (AAAI 1984), Austin, pp. 288–294 (1984)Google Scholar
  49. 49.
    Rosch, E., Mervais, C.B.: Family resemblance: Studies in the internal structure of categories. Cognitive Psychology 7, 573–605 (1975)CrossRefGoogle Scholar
  50. 50.
    Schank, R.C., Leake, D.B.: Creativity and Learning in a Case-Based Explainer. Artificial Intelligence 40(1-3), 353–385 (1989)CrossRefGoogle Scholar
  51. 51.
    Skalak, D.B., Rissland, E.L.: Arguments and Cases: An Inevitable Intertwining. Artificial Intelligence and Law 1(1), 3–44 (1992)CrossRefGoogle Scholar
  52. 52.
    Smith, E.E., Medin, D.L.: Categories and Concepts. Harvard University Press (1981)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Edwina L. Rissland
    • 1
  1. 1.Department of Computer ScienceUniversity of MassachusettsAmherstU.S.A.

Personalised recommendations