Abstract
In legal reasoning, different assumptions are often considered when reaching a final verdict and judgement outcomes strictly depend on these assumptions. In this paper, we propose an approach for generating a declarative model of judgements from past legal cases, that expresses a legal reasoning structure in terms of principle rules and exceptions. Using a logic-based reasoning technique, we are able to identify from given past cases different underlying defaults (legal assumptions) and compute judgements that cover all possible cases (including past cases) within a given set of relevant factors. The extracted declarative model of judgements can then be used to make deterministic automated inference on future judgements, as well as generate explanations of legal decisions.
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Notes
- 1.
where \(\alpha /2\) represents arguments as described in Definition 4.
- 2.
The full example with each ASP program’s output, and proofs for the lemmas and propositions can be found at http://wp.doc.ic.ac.uk/spike/technical-reports/.
- 3.
While we were able to compute the relevant attacks and arguments for casebase with greater than 100 cases, we were unable to generate the judgement theory from them using \(\varPi _{gen}\).
References
Ashley, K.D.: Reasoning with cases and hypotheticals in hypo. Int. J. Man Mach. Stud. 34, 753–796 (1991)
Bench-Capon, T., Prakken, H., Sartor, G.: Argumentation in legal reasoning. In: Simari, G., Rahwan, I. (eds.) Argumentation in Artificial Intelligence, pp. 363–382. Springer, US (2009)
Dimopoulos, Y., Kakas, A.: Learning non-monotonic logic programs: learning exceptions. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 122–137. Springer, Heidelberg (1995)
Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77, 321–357 (1995)
Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., Schneider, M.T.: Potassco: the potsdam answer set solving collection. AI Commun. 24(2), 107–124 (2011)
Horty, J.F.: Reasons and precedent. In: Ashley, K.D., van Engers, T.M. (eds.) Proceedings of the Conference of the 13th International Conference on Artificial Intelligence and Law, Pittsburgh, USA, pp. 41–50. ACM (2011)
Inoue, K.: Learning extended logic programs. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence, pp. 176–181 (1997)
Ohara, K., Taka, H., Babaguchi, N., Kitahashi, T.: Determination of general concept in learning default rules. In: Mizoguchi, R., Slaney, J.K. (eds.) PRICAI 2000. LNCS, vol. 1886, pp. 104–114. Springer, Heidelberg (2000)
Routen, T., Bench-Capon, T.J.M.: Hierarchical formalizations. Int. J. Man Mach. Stud. 35(1), 69–93 (1991)
Satoh, K.: Translating case-based reasoning into abductive logic programming. In: Proceedings of 12th European Conference on Artificial Intelligence, Budapest, Hungary, pp. 142–146 (1996)
Satoh, K.: Statutory interpretation by case-based reasoning through abductive logic programming. JACIII 1(2), 94–103 (1997)
Satoh, K.: Analysis of case-based representability of boolean functions by monotone theory. In: Richter, M.M., Smith, C.H., Wiehagen, R., Zeugmann, T. (eds.) ALT 1998. LNCS (LNAI), vol. 1501, pp. 179–190. Springer, Heidelberg (1998)
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: Bekki, D. (ed.) JSAI-isAI 2010. LNCS, vol. 6797, pp. 153–164. Springer, Heidelberg (2011)
Satoh, K., Kubota, M., Nishigai, Y., Takano, C.: Translating the Japanese presupposed ultimate fact theory into logic programming. In: JURIX 2009: The Twenty-Second Annual Conference on Legal Knowledge and Information Systems, The Netherlands, pp. 162–171. IOS Press (2009)
Toni, F., Sergot, M.: Argumentation and answer set programming. In: Balduccini, M., Son, T.C. (eds.) Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning. LNCS, vol. 6565, pp. 164–180. Springer, Heidelberg (2011)
Acknowledgment
This work was partially supported by JSPS KAKENHI Grant Numbers 26280091. The authors would like to thank Robert Kowalski for his valuable comments, and Kristijonas Cyras for his two examples, including Example 1, which indicated an error in the paper presented at the Eighth International Workshop on Juris-informatics (JURISIN 2014), Kanagawa, Japan.
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Athakravi, D., Satoh, K., Law, M., Broda, K., Russo, A. (2015). Automated Inference of Rules with Exception from Past Legal Cases Using ASP. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds) Logic Programming and Nonmonotonic Reasoning. LPNMR 2015. Lecture Notes in Computer Science(), vol 9345. Springer, Cham. https://doi.org/10.1007/978-3-319-23264-5_8
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