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Diagnosing and Treating Effect of Legal Rule-Based Revision

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Abstract

Since statutes are revised over time, a rule-base representing statutes requires revisions to update represented interpretations. As an extension of our paper in JSAI2021, we investigate an aspect of semantical changes and the effects of revision due to judicial interpretations in this article. We present an effect diagnosis based on the difference of two AA-CBR case-bases that are equivalent to the rule-base before and after the revision. These AA-CBR case-bases can be built in a tractable time by employing prototypical cases with judgement directed acyclic graphs. Following the effect diagnosis, we incorporate feedback from a user to determine which non-trivial effect is unintentional. Following that, we present two approaches to treat such effects, depending on whether the user would like to prevent all or partial non-trivial effects from the revision.

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Notes

  1. This can be extended into a ground atom but we use a proposition in this paper for ease of exposition.

  2. This representation is adopted for ease of exposition. The implicit sublease contract is a fictitious condition to illustrate multiple conditions. We use :- instead of \(\leftarrow\). The variables begin with uppercase letters. The predicates and the constants begin with lowercase letters.

References

  1. Al-Abdulkarim, L., Atkinson, K., Bench-Capon, T.: Accommodating change. Artif. Intell. Law 24(4), 409–427 (2016)

    Article  Google Scholar 

  2. Alchourrón, C.E., Gärdenfors, P., Makinson, D.: On the logic of theory change: partial meet contraction and revision functions. J. Symb. Log. 50(2), 510–530 (1985)

    Article  MathSciNet  Google Scholar 

  3. Apt, K.R., Blair, H.A., Walker, A.: Towards a theory of declarative knowledge. In: Foundations of Deductive Databases and Logic Programming, pp. 89–148. Morgan Kaufmann, Burlington (1988)

  4. Arisaka, R.: A belief revision technique to model civil code updates. In: JSAI International Symposium on Artificial Intelligence, pp. 204–216. Springer International Publishing, Cham (2015)

  5. Athakravi, D., Satoh, K., Law, M., Broda, K., Russo, A.: Automated inference of rules with exception from past legal cases using ASP. In: International Conference on Logic Programming and Nonmonotonic Reasoning, pp. 83–96. Springer International Publishing, Cham (2015)

  6. Bain, M., Muggleton, S.: Non-monotonic learning. Inductive Log. Program. 38, 145–153 (1992)

    Google Scholar 

  7. Baroni, P., Caminada, M., Giacomin, M.: An introduction to argumentation semantics. Knowl. Eng. Rev. 26(4), 365–410 (2011)

    Article  Google Scholar 

  8. Bench-Capon, T., Sartor, G.: A model of legal reasoning with cases incorporating theories and values. Artif. Intell. 150(1–2), 97–143 (2003)

    Article  Google Scholar 

  9. Berman, D.H., Hafner, C.D.: Understanding precedents in a temporal context of evolving legal doctrine. In: Proceedings of the 5th International Conference on Artificial Intelligence and Law, ICAIL ’95, pp. 42–51. Association for Computing Machinery, New York (1995)

  10. Bohner: Impact analysis in the software change process: a year 2000 perspective. In: 1996 Proceedings of International Conference on Software Maintenance, pp. 42–51. IEEE, USA (1996)

  11. Cyras, K., Satoh, K., Toni, F.: Abstract argumentation for case-based reasoning. In: Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning, pp. 243–254. AAAI Press (2016)

  12. Dalal, M.: Investigations into a theory of knowledge base revision: preliminary report. In: Proceedings of the Seventh National Conference on Artificial Intelligence, vol. 2, pp. 475–479. AAAI Press (1988)

  13. De Vos, M., Padget, J., Satoh, K.: Legal modelling and reasoning using institutions. In: JSAI International Symposium on Artificial Intelligence, pp. 129–140. Springer Berlin Heidelberg, Berlin (2010)

  14. Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–357 (1995)

    Article  MathSciNet  Google Scholar 

  15. Fungwacharakorn, W., Satoh, K.: Legal debugging in propositional legal representation. In: JSAI International Symposium on Artificial Intelligence, pp. 146–159. Springer International Publishing, Cham (2018)

  16. Fungwacharakorn, W., Satoh, K.: Generalizing culprit resolution in legal debugging with background knowledge. In: Legal Knowledge and Information Systems-JURIX 2020: The Thirty-third Annual Conference, Brno, Czech Republic, December 9-11, 2020, Frontiers in Artificial Intelligence and Applications, vol. 334, pp. 52–62. IOS Press (2020)

  17. Fungwacharakorn, W., Tsushima, K., Satoh, K.: On the legal revision in PROLEG program. In: 34th Proceedings of the Annual Conference of JSAI, vol. 2020, pp. 3G5ES104–3G5ES104. Japan Society of Artificial Intelligence (2020)

  18. Fungwacharakorn, W., Tsushima, K., Satoh, K.: Effects of legal revision in PROLEG. In: 35th Proceedings of the Annual Conference of JSAI, vol. 2020. Japan Society of Artificial Intelligence (2021)

  19. Fungwacharakorn, W., Tsushima, K., Satoh, K.: On semantics-based minimal revision for legal reasoning. In: Proceedings of the 18th International Conference on Artificial Intelligence and Law-ICAIL ’21. ACM (2021)

  20. Fungwacharakorn, W., Tsushima, K., Satoh, K.: Resolving counterintuitive consequences in law using legal debugging. Artif. Intell. Law 29(4), 541–557 (2021)

  21. Governatori, G., Maher, M.J., Antoniou, G., Billington, D.: Argumentation semantics for defeasible logic. J. Log. Comput. 14(5), 675–702 (2004)

    Article  MathSciNet  Google Scholar 

  22. Hafner, C.D., Berman, D.H.: The role of context in case-based legal reasoning: teleological, temporal, and procedural. Artif. Intell. Law 10(1), 19–64 (2002)

    Article  Google Scholar 

  23. Horty, J.F., Bench-Capon, T.J.: A factor-based definition of precedential constraint. Artif. Intell. Law 20(2), 181–214 (2012)

    Article  Google Scholar 

  24. Lehnert, S.: A taxonomy for software change impact analysis. In: Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th Annual ERCIM Workshop on Software Evolution, pp. 41–50. Association for Computing Machinery, New York (2011)

  25. Levi, E.H.: An Introduction to Legal Reasoning. University of Chicago Press, Chicago (2013)

    Book  Google Scholar 

  26. Li, T., Balke, T., De Vos, M., Padget, J., Satoh, K.: A model-based approach to the automatic revision of secondary legislation. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law, ICAIL ’13, pp. 202–206. Association for Computing Machinery, New York (2013)

  27. Maranhão, J.: Refinement: a tool to deal with inconsistencies. In: Proceedings of the 8th International Conference on Artificial Intelligence and Law, pp. 52–60. Association for Computing Machinery, New York (2001)

  28. Muggleton, S., Buntine, W.: Machine invention of first-order predicates by inverting resolution. In: Machine Learning Proceedings 1988, pp. 339–352. Elsevier (1988)

  29. Nebel, B., et al.: Syntax-based approaches to belief revision. Belief Revis. 29, 52–88 (1992)

    Article  MathSciNet  Google Scholar 

  30. Peppas, P., Williams, M.A., Chopra, S., Foo, N.: Relevance in belief revision. Artif. Intell. 229, 126–138 (2015)

    Article  MathSciNet  Google Scholar 

  31. Pettorossi, A., Proietti, M.: Transformation of logic programs: foundations and techniques. J. Log. Program. 19, 261–320 (1994)

    Article  MathSciNet  Google Scholar 

  32. Plotkin, G.D.: A note on inductive generalization. Mach. Intell. 5(1), 153–163 (1970)

    MathSciNet  MATH  Google Scholar 

  33. Rigoni, A.: An improved factor based approach to precedential constraint. Artif. Intell. Law 23(2), 133–160 (2015)

    Article  Google Scholar 

  34. Rissland, E.L., Friedman, M.T.: Detecting change in legal concepts. In: Proceedings of the 5th International Conference on Artificial Intelligence and Law, pp. 127–136. Association for Computing Machinery, New York (1995)

  35. Rotolo, A., Roversi, C.: Constitutive rules and coherence in legal argumentation: the case of extensive and restrictive interpretation. In: Dahlman, C., Feteris, E. (eds.) Legal Argumentation Theory: Cross-Disciplinary Perspectives, pp. 163–188. Springer, Dordrecht (2013)

  36. Satoh, K.: Nonmonotonic Reasoning by Minimal Belief Revision. Institute for New Generation Computer Technology, Tokyo (1988)

    Google Scholar 

  37. Satoh, K., Asai, K., Kogawa, T., Kubota, M., Nakamura, M., Nishigai, Y., Shirakawa, K., Takano, C.: PROLEG: an implementation of the presupposed ultimate fact theory of Japanese civil code by PROLOG technology. In: Onada, T., Bekki, D., McCready, E. (eds.) New Frontiers in Artificial Intelligence. Lecture Notes in Computer Science, pp. 153–164. Springer, Berlin (2011)

  38. Satoh, K., Kubota, M., Nishigai, Y., Takano, C.: Translating the Japanese Presupposed Ultimate Fact Theory into logic programming. In: Proceedings of the 2009 Conference on Legal Knowledge and Information Systems: JURIX 2009: The Twenty-Second Annual Conference, pp. 162–171. IOS Press, Amsterdam (2009)

  39. Sergot, M.J., Sadri, F., Kowalski, R.A., Kriwaczek, F., Hammond, P., Cory, H.T.: The British Nationality Act as a logic program. Commun. ACM 29(5), 370–386 (1986)

    Article  Google Scholar 

  40. Sherman, D.M.: A prolog model of the income tax act of Canada. In: Proceedings of the 1st International Conference on Artificial Intelligence and Law, pp. 127–136. Association for Computing Machinery, New York (1987)

  41. Tamaki, H., Sato, T.: Unfold/fold transformation of logic programs. In: Proceedings of the Second International Conference on Logic Programming, pp. 127–138. Uppsala University, Uppsala (1984)

  42. Thielscher, M.: The qualification problem: a solution to the problem of anomalous models. Artif. Intell. 131(1–2), 1–37 (2001)

    Article  MathSciNet  Google Scholar 

  43. Verheij, B.: Correct grounded reasoning with presumptive arguments. In: European Conference on Logics in Artificial Intelligence, pp. 481–496. Springer International Publishing, Cham (2016)

  44. Verheij, B.: Formalizing value-guided argumentation for ethical systems design. Artif. Intell. Law 24(4), 387–407 (2016)

    Article  Google Scholar 

  45. Verheij, B.: Formalizing arguments, rules and cases. In: Proceedings of the 16th Edition of the International Conference on Artificial Intelligence and Law, ICAIL ’17, p. 199-208. Association for Computing Machinery, New York (2017)

  46. Wogulis, J., Pazzani, M.J.: A methodology for evaluating theory revision systems: results with audrey II. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1128–1134. Morgan Kaufmann, San Francisco (1993)

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Acknowledgements

This work was supported by JSPS KAKENHI Grant numbers, JP17H06103 and JP19H05470 and JST, AIP Trilateral AI Research, Grant number JPMJCR20G4, Japan.

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  1. National Institute of Informatics, Sokendai University, Tokyo, Japan

    Wachara Fungwacharakorn, Kanae Tsushima & Ken Satoh

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  1. Wachara Fungwacharakorn
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  2. Kanae Tsushima
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  3. Ken Satoh
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Correspondence to Wachara Fungwacharakorn.

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Fungwacharakorn, W., Tsushima, K. & Satoh, K. Diagnosing and Treating Effect of Legal Rule-Based Revision. New Gener. Comput. 40, 25–45 (2022). https://doi.org/10.1007/s00354-022-00157-3

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