Abstract
In this paper we discuss a view of the Machine Learning technique called Explanation-Based Learning (EBL) or Explanation-Based Generalization (EBG) as a process for the interpretation of vague concepts in logic-based models of law.
The open-textured nature of legal terms is a well-known open problem in the building of knowledge-based legal systems. EBG is a technique which creates generalizations of given examples on the basis of background domain knowledge. We relate these two topics by considering EBG's domain knowledge as corresponding to statute law rules, and EBG's training example as corresponding to a precedent case.
By making the interpretation of vague predicates as guided by precedent cases, we use EBG as an effective process capable of creating a link between predicates appearing as open-textured concepts in law rules, and predicates appearing as ordinary language wording for stating the facts of a case.
Standard EBG algorithms do not change the deductive closure of the domain theory. In the legal context, this is only adequate when concepts vaguely defined in some law rules can be reformulated in terms of other concepts more precisely defined in other rules. We call ‘theory reformulation’ the process adopted in this situation of ‘complete knowledge’.
In many cases, however, statutory law leaves some concepts completely undefined. We then propose extensions to the EBG standard that deal with this situation of ‘incomplete knowledge’, and call ‘theory revision’ the extended process. In order to fill in ‘knowledge gaps’ we consider precedent cases supplemented by additional heuristic information. The extensions proposed treat heuristics represented by abstraction hierarchies with constraints and exceptions.
In the paper we also precisely characterize the distinction between theory reformulation and theory revision by stating formal definitions and results, in the context of the Logic Programming theory.
We offer this proposal as a possible contribution to cross fertilization between machine learning and legal reasoning methods.
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References
Ashley, K. D. & Rissland, E. L. 1986. Toward Modelling Legal Arguments. InAutomated Analysis of Legal Texts, ed., Martino A. Socci Natali F. 19–30. North Holland.
Bench-Capon, T. J. M. & Sergot, M. J. 1985.Towards a Rule-based Representation of Open Texture in law. Imperial College, Dept. of Computing, London.
Bertarello, S. 1993. Estensione delle tecniche di Explanation-Based Learning mediante l'uso dell'abduzione. Degree Thesis in Computer Science (G. A. Lanzarone, S. Costantini supervisors), University of Milano.
Bertarello, S., Costantini, S. & Lanzarone, G. A. 1994.Explanation-Based Learning of Open-Textured Predicates in Logic Programming Models of Law. In Proceedings ofThe Workshop on Legal Applications of Logic Programming, ed. C. Biagioli, G. Sartor, & D. Tiscornia. 14–32. Held in conjunction with ICLP'94, Santa Margherita Ligure, Italy, June 13–18.
Branting, L. K., Porter, B. W. 1991. Rules and Precedents as Complementary Warrants. In Proceedings ofThe Ninth Nat. Conf. on Artificial Intelligence, vol. I, 3–9. MIT Press.
Bruynooghe, M., De Raedt, L. & De Schreye, D. 1989. Explanation Based Program Transformation. In ProceedingsIJCAI '89. vol. I. 407–412. Morgan Kaufmann.
Costantini, S. & Lanzarone, G. A. 1992. Analogical Reasoning in Reflective Prolog. InExpert Systems in Law, ed. A. A. Martino, 133–145. Elsevier.
Costantini, S. & Lanzarone, G. A. 1994. A MetaLogic Programming Approach: Language, Semantics and Applications.Journal of Experimental and Theoretical Artificial Intelligence 6: 239–287.
DeJong, G. & Mooney, R. 1986. Explanation-Based Learning: An Alternative View.Machine Learning 1: 145–176.
Duval, B. & Kodratoff, Y. 1990. A Tool for the Management of Incomplete Theories: Reasoning about Explanation. InMachine Learning, Meta-Reasoning and Logics, eds. P. B. Brazdil & K. Konolige. 135–158. Kluwer Academic Publishers.
Ellman, T. 1989. Explanation-Based Learning: a Survey of Programs and Perspectives.ACM Computing Surveys, 21 (2): 163–221.
Gardner, A. 1987.An Artificial Intelligence Approach to Legal Reasoning. MIT Press.
Hage, J. C., Leenes, R. & Lodder, A. R. 1994. Hard Cases: A Procedural Approach.Artificial Intelligence and Law, 2: 113–167.
Hart, H. L. A. 1961.The Concept of Law. Oxford: Clarendon Press.
Hirsh, H. 1987. Explanation-Based Generalization in a Logic Programming Environment. In Proceedings ofThe Tenth Int. Joint Conf. on Artificial Intelligence, 221–227. Milano, August 23–28.
Kedar-Cabelli, S. T. & McCarty, L. T. 1987. Explanation-Based Generalization as Resolution Theorem Proving. In Proceedings of the4th Int. Workshop on Machine Learning, 383–389. Irvine (CA), June 22–25, Morgan Kaufmann.
Kowalski, R. & Sergot, M. 1989.The Use of Logical Models in Legal Problem Solving. Expert Systems in Law, Int. Conf. on Law and Artificial Intelligence, Bologna.
Lloyd, J. W. 1987.Foundations of Logic Programming. 2nd edition, Springer-Verlag.
Maher, M. J. 1986.Equivalence of Logic Programs. Third Int. Conf. on Logic Programming.
Michalski, R. 1994.Inferential Theory of Learning: Developing Foundations for Multistrategy Learning. In [Michalski & Tecuci 1994], 3–61.
Michalski, R. & Tecuci, G. eds. 1994.Machine Learning — A Multistrategy Approach — vol. IV. Morgan Kaufman.
Mitchell, T. M., Keller, R. M. & Kedar-Cabelli, S. T. 1986. Explanation-Based Generalization: A Unifying View.Machine Learning 1 (1): 47–80.
Oskamp, A., Walker, R. F., Schricks, J. A. & van den Berg, P. H. 1989. Prolex, Devide and Rule: A Legal Application. In Proceedings ofThe 2nd Int. Conf. on Artificial Intelligence and Law, 54–62, ACM Press.
Prieditis, A. E. & Mostow, J.PROLEARN: Towards a Prolog Interpreter that Learns. AAAI 87, 494–498.
Rissland, E. L. & Skalak, D. B. 1989. Combining Case-Based and Rule-Based Reasoning: A Heuristic Approach. ProceedingsIJCAI 524–530.
Sergot, M. J., Sadri, F., Kowalski, R. A., Kziwaczek, F., Hammond, P. & Cory, H. T. 1986.The British Nationality Act as a Logic Program.CACM, 29 (5): 370–386.
Skalak, D. B. & Rissland, E. L. 1992. Using Case-Based Reasoning to Extend the Expertise of Expert Systems. InExpert Systems in Law, ed. A. A. Martino, 321–338. Elsevier.
Southwick, R. W.The Lemma Generation Problem. Imperial College Technical Report, London.
Susskind, R. 1986Expert Systems in Law. Oxford University Press.
van Harmelen, F. & Bundy, A. 1988. Explanation Based Generalization = Partial Evaluation.Artficial Intelligence 36: 401–412.
Waterman, D. A. & Peterson, M. A. 1980. Rule-Based Models of Legal Expertise. In Proceedings ofThe First Annual Conf. on Artificial Intelligence, 272–275. Standford.
Waterman, D. A. & Peterson, M. A. 1986. Models of Legal Decisionmaking. InExpert Systems, Techniques, Tools and Applications, eds. P. Klahr, D. A. Waterman. 135–185. Addison Wesley.
Winograd, T. 1983. Frame Representations and the Declarative/Procedural Controversy. In ProceedingsIJCAI.
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Costantini, S., Lanzarone, G.A. Explanation-based interpretation of open-textured concepts in logical models of legislation. Artif Intell Law 3, 191–208 (1995). https://doi.org/10.1007/BF00872530
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DOI: https://doi.org/10.1007/BF00872530