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Case-based reasoning and its implications for legal expert systems

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Abstract

Reasoners compare problems to prior cases to draw conclusions about a problem and guide decision making. All Case-Based Reasoning (CBR) employs some methods for generalizing from cases to support indexing and relevance assessment and evidences two basic inference methods: constraining search by tracing a solution from a past case or evaluating a case by comparing it to past cases. Across domains and tasks, however, humans reason with cases in subtly different ways evidencing different mixes of and mechanisms for these components.

In recent CBR research in Artificial Intelligence (AI), five paradigmatic approaches have emerged: statistically-oriented, model-based, planning/design-oriented, exemplar-based, and adversarial or precedent-based. The paradigms differ in the assumptions they make about domain models, the extent to which they support symbolic case comparison, and the kinds of inferences for which they employ cases.

Reasoning with cases is important in legal practice of all kinds, and legal practice involves a wide variety of case-based tasks and methods. The paradigms' respective benefits and costs suggest different approaches for different legal tasks.

CBR research and development in the field of AI and Law should be pursued vigoriously for several reasons. CBR can supplement rule-based expert systems, improving their abilities to reason about statutory predicates, solve problems efficiently, and explain their results. CBR can also contribute to the design of intelligent legal data retrieval systems and improve legal document assembly programs. Finally, in cognitive studies of various fields, it can model methods of transforming ill-structured problems into better structured ones through the use of case comparisons.

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References

  • Aleven, V. & Ashley, K. D. 1992. Automated Generation of Examples for a Tutorial in Case-Based Argumentation. In Proceedings of the Second International Conference on Intelligent Tutoring Systems. Montreal.

  • Allen, L. E. & Engholm, C. R. 1978. Normalized Legal Drafting and the Query Method. Journal of Legal Education 29:380–412.

    Google Scholar 

  • Allen, L. E. & Saxon, C. S. 1987. Some Problems in Designing Expert Systems to Aid Legal Reasoning. In First International Conference on Artificial Intelligence and Law, 94–103, Northeastern University, Boston.

    Google Scholar 

  • Ashley, K. D. & Aleven, V. 1991. Toward an Intelligent Tutoring System for Teaching Law Students to Argue with Cases. In Proceedings of the Third International Conference on Articial Intelligence and Law, 42–52, Oxford, England.

  • Ashley, K. D. & Aleven, V. 1992. Generating Dialectical Examples Automatically. In Proceedings AAAI-92. American Association for Artificial Intelligence, San Jose, CA.

    Google Scholar 

  • Ashley, K. D. & Rissland, E. L. 1987. Compare and Contrast, A Test of Expertise. In Proceedings AAAI-87. American Association for Artificial Intelligence, Seattle.

    Google Scholar 

  • Ashley, K. D. & Rissland, E. L. 1988. A Case-Based Approach to Modeling Legal Expertise. IEEE Expert. Vol. 3, Nr. 3.

  • Ashley, K. D. & Rissland, E. L. 1988. Waiting on Weighting: A Symbolic Least Commitment Approach. In Proceedings AAAI-88. American Association for Artificial Intelligence, St. Paul.

    Google Scholar 

  • Ashley, K. D. 1989. Defining Salience in Case-Based Arguments. In Proceedings IJCAI-89. International Joint Conferences on Artificial Intelligence, Detroit.

    Google Scholar 

  • Ashley, K. D. 1990. Modeling Legal Argument: Reasoning with Cases and Hypotheticals. MIT Press, Cambridge. Based on Ashley's 1987 PhD. Dissertation, University of Massachusetts, COINS Technical Report No. 88–01.

    Google Scholar 

  • Ashley, K. D. 1991. Reasoning with Cases and Hypotheticals in HYPO. International Journal of Man-Machine Studies 34 (6):753–796.

    Google Scholar 

  • Bareiss, E. R., Porter, B. W. & Wier, C. C. 1988. Protos: An Exemplar-Based Learning Apprentice. International Journal of Man-Machine Studies 29:549–561.

    Google Scholar 

  • Bareiss, E. R., Porter, B. W. & Murray, K. S. 1989. Supporting Start-to-Finish Development of Knowledge Bases. Machine Learning 4:259–283.

    Google Scholar 

  • Bareiss, E. R. 1988. Exemplar-Based Knowledge Acquisition — A Unified Approach to Concept Representation, Classification, and Learning. Academic Press, San Diego, CA. Based on PhD thesis, University of Texas.

    Google Scholar 

  • Barletta, R. & Mark, W. 1988. Explanation-Based Indexing of Cases. In Proceedings of the Case-Based Reasoning Workshop, 50–60, Clearwater Beach, FL. Defense Advanced Research Projects Agency — Information Science and Technology Office.

    Google Scholar 

  • Beauchamp, T. & McCullough, B. 1984. Medical Ethics: The Moral Responsibilities of Physicians. Prentice-Hall, Englewood Cliffs, NJ.

    Google Scholar 

  • Bellairs, K. 1989. Contextual Relevance in Analogical Reasoning: A Model of Legal Argument. PhD thesis, University of Minnesota.

  • Berman, D. H. & Hafner, C. 1986. Obstacles to the Development of Logic-Based Models of Legal Reasoning. In Charles Walter (ed.), Computer Power and Legal Language, 185–214. New York: Quorum Books.

    Google Scholar 

  • Berman, D. H. & Hafner, C. D. 1991. Incorporating Procedural Context into a Model of Case-Based Legal Reasoning. In Proceedings of the Third International Conference on Artificial Intelligence and Law, 12–20, Oxford, England.

  • Blair, D. C. & Maron, M. E. 1985. An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System. Communications of the ACM 28(3): 289–299.

    Google Scholar 

  • Branting, L. K. & Porter, B. W. 1991. Rules and Precedents as Complementary Warrants. In Proceedings AAAI-91, 3–9. American Association for Artificial Intelligence, Anaheim.

    Google Scholar 

  • Branting, L. K. 1991. Building Explanations from Rules and Structured Cases. International Journal of Man-Machine Studies 34(6): 797–837.

    Google Scholar 

  • Branting, L. K. 1991. Integrating Rules and Precedents for Classification and Explanation: Automating Legal Analysis. PhD thesis, The University of Texas at Austin. Artificial Intelligence Laboratory, Technical Report No. A190-146.

  • Buchanan, B. G. & Headrick, T. E. 1970. Some Speculation about Artificial Intelligence and Legal Reasoning. Stanford Law Review 23: 40–62.

    Google Scholar 

  • Burton, S. J. 1985. An Introduction to Law and Legal Reasoning. Little, Brown, Boston.

    Google Scholar 

  • Capper, P. N. & Susskind, R. E. 1988. Latent Damage Law — The Expert System. London: Butterworths.

    Google Scholar 

  • Carbonell, J. & Veloso, M. 1988. Integrating Derivational Analogy into a General Problem Solving Architecture. In Proceedings of the Case-Based Reasoning Workshop, Clearwater Beach, FL. DARPA/ISTO.

    Google Scholar 

  • Carbonell, J. G. 1983. Derivational Analogy and Its Role in Problem Solving. In Proceedings AAAI-83, Washington, DC. American Association for Artificial Intelligence.

    Google Scholar 

  • Clancey, W. J. 1983. The epistemology of a rule-based expert system: A framework for explanation. Artificial Intelligence 20: 215–251. Reprinted in: Buchanan B. G. & Shortliffe E. H. (eds.), Rule-Based Expert Systems; The MYCIN Experiments of the Stanford Heuristic Programming Project.

    Google Scholar 

  • Collins, A. & Stevens, A. L. 1982. Goals and Strategies of Inquiry Teachers. In Robert Glaser, editor, Advances in Instructional Psychology, Volume 2. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Domeshek, E. A. 1991. Indexing Stories as Social Advice. In Proceedings AAAI-91, 16–21. Anaheim: American Association for Artificial Intelligence.

    Google Scholar 

  • Dworkin, R. 1978. No Right Answer? New York University Law Review 53:1–32.

    Google Scholar 

  • Eisenberg, T. & Henderson, Jr., J. A. 1992. Inside the Quiet Revolution in Products Liability. UCLA Law Review 39: 731.

    Google Scholar 

  • Ernst, G. W. & Newell, A. 1969. GPS: A Case Study in Generality and Problem Solving. New York: Academic Press, ACM Monograph Series.

    Google Scholar 

  • Gansler, J. S. 1991. Acquisition of High Technology Equipment: An Application for Case-Based Reasoning. Address presented at DARPA Workshop on Case-Based Reasoning. Washington, D.C.

  • Gardner, A. vdL. 1987. An Artificial Intelligence Approach to Legal Reasoning. Cambridge: MIT Press.

    Google Scholar 

  • Golding, A. R. & Rosenbloom, P. S., 1991. Improving Rule-Based Systems through Case-Based Reasoning. In Proceedings AAAI-91, 22–27. Anaheim: American Association for Artificial Intelligence.

    Google Scholar 

  • Greenleaf, G., Mowbray, A. & Tyree, A. 1991. The DataLex Legal Workstation — Integrating Tools for Lawyers. In Proceedings of the Third International Conference on Artificial Intelligence and Law, 215–224, Oxford, England.

  • Hammond, K. J. 1986. CHEF: A Model of Case-based Planning. In Proceedings AAAI-86, 267–271.Philadelphia: American Association for Artificial Intelligence.

    Google Scholar 

  • Hammond, K. J. 1986. Learning to Anticipate and Avoid Planning Problems through the Explanation of Failures. In Proceedings AAAI-86, 556–560. Philadelphia: American Association for Artificial Intelligence.

    Google Scholar 

  • Hammond, K. J. 1987. Explaining and Repairing Plans That Fail. In Proceedings IJCAI-87. Milan: International Joint Conferences on Artificial Intelligence.

    Google Scholar 

  • Hammond, K. J. 1989. Case-Based Planning — Viewing Planning as a Memory Task. San Diego, CA: Academic Press.

    Google Scholar 

  • Henderson, J. A. Jr. & Eisenberg, T. 1990. The Quiet Revolution in Products Liability: An Empirical Study of Legal Change. UCLA Law Review 37:479.

    Google Scholar 

  • Henrion, M., Breese, J. S. & Horvitz, E. J. 1991. Decision Analysis and Expert Systems. AI Magazine 12(4): 64–91.

    Google Scholar 

  • Hinrichs, T. R. & Kolodner, J. L. 1991. The Roles of Adaptation in Case-Based Design. In Proceedings AAAI-91, 28–33. Anaheim: American Association for Artificial Intelligence.

    Google Scholar 

  • Jonsen, A. R. & Toulmin, S. 1988. The Abuse of Casuistry A History of Moral Reasoning. Berkeley: University of California Press.

    Google Scholar 

  • Kass, A. M. Leake, D. & Owens, C. C. 1986. Swale: A Program that Explains. In Roger C. Schanck, editor, Explanation Patterns: Understanding Mechanically and Creatively. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Kedar-Cabelli, S. 1984. Analogy with Purpose in Legal Reasoning from Precedents: A Dissertation Proposal. Technical Report LRP-TR-17, Laboratory for Computer Science Research, Rutgers University.

  • Kolodner, J. L. Simpson, R. L. & Sycara-Cyranski, K. 1985. A Process Model of Case-Based Reasoning in Problem Solving. In Proceedings IJCAI-85, 284–290, Los Angeles: International Joint Conferences on Artificial Intelligence.

    Google Scholar 

  • Kolodner, J. L. 1983. Maintaining Organization in a Dynamic Long-Term Memory. Cognitive Science 7(4): 243–280.

    Google Scholar 

  • Kolodner, J. L. 1983. Reconstructive Memory: A Computer Model. Cognitive Science, 7(4):281–328.

    Google Scholar 

  • Koton, P. 1988. Reasoning about Evidence in Causal Explanations. In Proceedings of the Case-Based Reasoning Workshop, 260–270, Clearwater Beach, FL: DARPA/ISTO.

    Google Scholar 

  • Koton, P. 1988. Reasoning about Evidence in Causal Explanations. In Proceedings AAAI-88, 256–261. St. Paul, MN: American Association for Artificial Intelligence.

    Google Scholar 

  • Koton, P. 1988. Using Experience in Learning and Problem Solving. PhD thesis, MIT.

  • Kuhn, T.S. 1980. The Structure of Scientific Revolutions. University of Chicago Press.

  • Lakatos, I. 1976. Proofs and Refutations. London: Cambridge University Press.

    Google Scholar 

  • Leake, D. B. 1991. An Indexing Vocabulary for Case-Based Explanation. In Proceedings AAAI-91, 10–15. Anaheim: American Association for Artificial Intelligence.

    Google Scholar 

  • Lesgold, A. M., Bonar, J. G., Ivill, J. M. & Bowen, A. 1987. An intelligent tutoring system for electronics troubleshooting: DC-circuit understanding. Technical report, Learning Research and Development Center, University of Pittsburgh.

  • Levi, E. L. 1949. An Introduction to Legal Reasoning. University of Chicago Press.

  • Llewellyn, K. N. 1930. The Bramble Bush: On Our Law and Its Study. Dobbs Ferry, NY: Oceana Publications, 1960 edition.

    Google Scholar 

  • Llewellyn, K. N. 1938. On Our Case Law of Contract: Offer and Acceptance. Yale Law Journal 48: 1–36, 779–818.

    Google Scholar 

  • Llewellyn, K. N. 1989. The Case Law System in America. Chicago: University of Chicago Press. Recent translation of Praejudizienrecht und Rechtssprechung in Amerika, 1933.

    Google Scholar 

  • MacGregor, R. M. 1988. A Deductive Pattern Matcher. In Proceedings AAAI-88, 403–408, Saint Paul, MN: American Association for Artificial Intelligence.

    Google Scholar 

  • Mark, W. & Schlossberg, J. 1990. A Design Memory Without Cases. In Proceedings of the Spring Symposium Series, 51–55. Palo Alto: American Association for Artificial Intelligence.

    Google Scholar 

  • Mark, W. S. 1989. Case-Based Reasoning for Autoclave Management. In Proceedings of the Case-Based Reasoning Workshop, 176–180, Pensacola Beach, FL: Defense Advanced Research Projects Agency — Information Science and Technology Office.

    Google Scholar 

  • McCarty, L. T. & Sridharan, N. S. 1981. The Representation of an Evolving System of Legal Concepts: II. Prototypes and Deformations. In Proceedings IJCAI-81, Vancouver, BC: International Joint Conferences on Artificial Intelligence.

    Google Scholar 

  • McCarty, L. T. & Sridharan, N. S. 1982. A Computational Theory of Legal Argument. Technical Report LRP-TR-13, Laboratory for Computer Science Research, Rutgers University.

  • McCarty, L. T. 1980. A Computational Theory of Eisner v. Macomber. Technical Report LRP-TR-9, Laboratory for Computer Science Research, Rutgers University.

  • McKeown, K. R., Wish, M. & Matthews, K. 1985. Tailoring Explanations for the User. In Proceedings IJCAI-85, Los Angeles: International Joint Conferences on Artificial Intelligence.

    Google Scholar 

  • Miller, R. A., Schaffner K. F. & Meisel, A. 1985. Ethical and Legal Issues Related to the Use of Computer Programs in Clinical Medicine. Annals of Internal Medicine 102(4): 529–536.

    Google Scholar 

  • Mital, V., Stylianou, A. & Johnson, L. 1991. Conceptual Information Retrieval in Litigation Support Systems. In Proceedings of the Third International Conference on Artificial Intelligence and Law; 235–243, Oxford, England.

  • Moore, J. D. & Swartout, W. R. 1989. A Reactive Approach to Explanation. In Proceedings of the 11th International Joint Conference on Artificial Intelligence, Detroit, MI. Reprinted as Tech. Rep. ISI/RS-89–239, Information Sciences Institute, University of Southern California, Marina del Rey, CA.

    Google Scholar 

  • Moore, J. D. & Swartout, W. R. 1990. A Reactive Approach to Explanation: Taking the User's Feedback into Account. In Paris, C., Swartout, W. & Mann, W. (eds), Natural Language Generation in Artificial Intelligence and Computational Linguistics, 1–48. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Moore, J. D. & Swartout, W. R. 1990. Pointing: A Way Toward Explanation Dialogue. In Proceedings AAAI-90, 457–464. Boston: American Association for Artificial Intelligence.

    Google Scholar 

  • Moore, A. 1989. Trial by Schema: Cognitive Filters in the Courtroom. UCLA Law Review 37:273.

    Google Scholar 

  • Neustadt, R. E. & May, E. R. 1986. Thinking in Time. New York: Free Press.

    Google Scholar 

  • Nimmer, R. & Krauthaus, P. A. 1986. Computer Error and User Liability Risk. Jurimetrics 26(2): 121–137.

    Google Scholar 

  • Owens, C. 1988. Domain-Independent Prototype Cases for Planning. In Proceedings of the Case-Based Reasoning Workshop, 302–311, Clearwater Beach, FL: DARPA/ISTO.

    Google Scholar 

  • Radin, M. 1933. Case Law and Stare Decisis. Columbia Law Review 33:199.

    Google Scholar 

  • Riesbeck, C. K. & Schank, R. C. 1989. Inside Case-Based Reasoning. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Riloff, E. & Lehnert, W. 1992. Classifying Texts Using Relevancy Signatures. In Proceedings AAAI-92. San Jose, CA: American Association for Artificial Intelligence.

    Google Scholar 

  • Rissland, E. L. & Ashley, K. D. 1986. Hypotheticals as Heuristic Device. In Proceedings AAAI-86. Philadelphia: American Association for Artificial Intelligence.

    Google Scholar 

  • Rissland, E. L. & Skalak, D. B. 1989. Combining Case-Based and Rule-Based Reasoning: A Heuristic Approach. In Proceedings IJCAI-89. Detroit: International Joint Conferences on Artificial Intelligence.

    Google Scholar 

  • Rissland, E. L. & Skalak, D. B. 1991. CABARET: Rule Interpretation in a Hybrid Architecture. International Journal of Man-Machine Studies 34(6):839–887.

    Google Scholar 

  • Rissland, E. L. & Soloway, E. M. 1980. Overview of an Example Generation System. In Proceedings AAAI-80, Stanford, CA: American Association for Artificial Intelligence.

    Google Scholar 

  • Rissland, E. L. 1981. Constrained Example Generation. Technical Report 81–24, Computer and Information Science Department, University of Massachusetts, Amherst, MA.

    Google Scholar 

  • Rissland, E. L. 1990. Artificial Intelligence and Law: Stepping Stones to a Model of Legal Reasoning. Yale Law Journal 99(8):1957–1981.

    Google Scholar 

  • Rose, D. E. & Belew, R. K. 1991. A Connectionist and Symbolic Hybrid for Improving Legal Research. International Journal of Man-Machine Studies 35(1):1–33.

    Google Scholar 

  • Sanders, K. E. 1991. Within the Letter of the Law: Reasoning Among Multiple Cases. In Proceedings of the Case-Based Reasoning Workshop, 317–326, Washington, DC: Defense Advanced Research Projects Agency — Information Science and Technology Office.

    Google Scholar 

  • Schaffner, K. F. 1990. Case-Based Reasoning in Law and Ethics. Presentation at the ‘Foundations of Bioethics’ Conference. Hastings Center.

  • Schank, R. C. & Abelson, R. 1977. Scripts, Plans, Goals and Understanding. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Schank, R. 1984. The Cognitive Computer: On Language, Learning and Artificial Intelligence. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Sergot, M. J., Sadri, F., Kowalski, R. A., Kriwaczek, F., Hammond, P. & Cory, H. T. 1986. The British Nationality Act as a Logic Program. Communications of the ACM 29(5):370–386.

    Google Scholar 

  • Shortliffe, E. H. 1984. Details of the Consultation System. In Buchanan, Bruce G. and Shortliffe, Edward H. (eds.), Rule-Based Expert Systems, 78–132. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Simoudis, E. & Miller, J. S. 1991. The Application of CBR to Help Desk Applications. In Proceedings of the Case-Based Reasoning Workshop, 25–36. Washington, DC: Defense Advanced Research Projects Agency — Information Science and Technology Office.

    Google Scholar 

  • Simpson, R. L. 1985. A Computer Model of Case-Based Reasoning in Problem Solving. PhD thesis, Georgia Institute of Technology. GIT-ICS-85/18.

  • Sinha, A. P. 1993. IDEA: A Case-Based Model of Design Assistance. PhD thesis, University of Pittsburgh, Katz Graduate School of Business. Pittsburgh.

    Google Scholar 

  • Smith, J. C. & Deedman, C. 1987. The Application of Expert Systems Technology to Case-Based Law. In First International Conference on Artificial Intelligence and Law. Boston: Northeastern University.

    Google Scholar 

  • Stanfill, C. & Waltz, D. 1986. Toward Memory-Based Reasoning. Communications of the ACM 29(12): 1213–1228.

    Google Scholar 

  • Stanfill, C. W. 1987. Memory-Based Reasoning Applied to English Pronunciation. In Proceedings AAAI-87, 577–581. Seattle: American Association for Artificial Intelligence.

    Google Scholar 

  • Strong, C. 1988. Justification in Ethics. In Baruch A. Brody, editor, Moral Theory and Moral Judgments in Medical Ethics, 193–211. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • SUP. 1985. The Complete Oral Arguments of the Supreme Court of the United States. Frederick, MD: University Publications of America.

    Google Scholar 

  • Susskind, R. E. 1987. Expert Systems in Law: a Jurisprudential Inquiry. Oxford: Oxford University Press.

    Google Scholar 

  • Swartout, W. R. 1983. XPLAIN: a System for Creating and Explaining Expert Consulting Programs. Artificial Intelligence 21(3): 285–325.

    Google Scholar 

  • Sycara, K. & Navinchandra, D. 1991. Influences: A Thematic Abstraction for Creative Use of Multiple Cases. In Proceedings of the Case-Based Reasoning Workshop, 133–144. Washington, DC: Defense Advanced Research Projects Agency-Information Science and Technology Office.

    Google Scholar 

  • Sycara, K. 1987. Resolving Adversarial Conflicts: An Approach Integrating Case-Based and Analytic Methods. PhD thesis, Georgia Institute of Technology. School of Information and Computer Science, Technical Report No. 87–26. To be published under the title Case-Based Reasoning: A Study in Conflict Resolution, Lawrence Erlbaum Associates, Hillsdale NJ.

    Google Scholar 

  • Sycara, K. 1989. Argumentation: Planning Other Agents' Plans. In Proceedings IJCAI-89, 517–523. Detroit: International Joint Conferences on Artificial Intelligence.

    Google Scholar 

  • Sycara, K. 1989. Resolving Goal Conflicts via Negotiation. In Proceedings AAAI-88, 245–250. St. Paul: American Association for Artificial Intelligence.

    Google Scholar 

  • Veloso, M. M. & Carbonell, J. G. 1991. Variable-Precision Case Retrieval in Analogical Problem Solving. In Proceedings of the Case-Based Reasoning Workshop, 93–106, Washington, DC: Defense Advanced Research Projects Agency — Information Science and Technology Office.

    Google Scholar 

  • Vossos, G., Zeleznikow, J., Dillon, T. & Vossos, V. 1991. An Example of Integrating Legal Case Based Reasoning with Object-Oriented Rule-Based Systems: IKBALS II. In Proceedings of the Third International Conference on Articial Intelligence and Law, 31–41, Oxford, England.

  • Walker, R. F., Oskamp, A., Schrickx, J. A., van Opdorp, G. J. & van den Berg, P. H. 1991. Prolexs: Creating Law and Order in a Heterogeneous Domain. International Journal of Man-Machine Studies 35(1):35–68.

    Google Scholar 

  • Waterman, D. A. & Peterson, M. 1980. Rule-Based Models of Legal Expertise. In Proceedings AAAI-80, Stanford, CA: American Association for Artificial Intelligence.

    Google Scholar 

  • Waterman, D. A. & Peterson, M. 1981. Models of Legal Decisionmaking. Technical Report R-2717–1CJ, Santa Monica, CA: Rand Corporation.

    Google Scholar 

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  1. Assistant Professor of Law and Intelligent Systems, University of Pittsburgh, School of Law, USA

    Kevin D. Ashley

  2. Learning Research and Development Center, 15260, Pittsburgh, PA, U.S.A.

    Kevin D. Ashley

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Ashley, K.D. Case-based reasoning and its implications for legal expert systems. Artif Intell Law 1, 113–208 (1992). https://doi.org/10.1007/BF00114920

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