Modelling artificial legal reasoning
- 132 Downloads
As part of the KADS library a generic model for reasoning in assessment tasks has been proposed [Breuker et al., 1987], and recently refined [Valente & Lockenhoff, 1993]. Most legal reasoning tasks are assessement tasks. An architecture has been developed for legal reasoning: TRACS [Breuker & denHaan, 1991]. In this architecture, a distinction is made between reasoning about a (legal) world (case) and about the legal consequences (applying regulations). For each subtask different knowledge bases are required. The World KB contains a (terminological) description of a legal world, i.e. some social subsystem, like traffic or social security. The regulations that refer to this legal world are represented (isomorphically) in the Regulation KB. This separation of reasoning and knowledge bases makes artificial legal reasoning tractable, and in particular the way regulations are applied is very efficient, and is equivalent to the use of deontic logic [denHaan, 1993].
The TRACS architecture maps easily onto the KADS conceptual model of assessment. However, in the KADS model it is assumed that “norms” (regulations) can be specified from the model of the world. This is not true. These norms are external to that world, and cannot be derived from it, as can be in diagnosis tasks. Advantages and problems in knowledge acquisition for legal assessement tasks are discussed. Because regulations can be represented isomorphically with their textual source, knowledge acquisition appears very unproblematic. However, modelling a legal world, i.e. the social system that the regulation is supposed to control, is more difficult, because this social system and the presuppositions about its working is always largely implicit.
KeywordsKnowledge Acquisition Legal Norm Assessment Task World Model Legal Reasoning
Unable to display preview. Download preview PDF.
- [Bench-Capon & Coenen, 1991]T. Bench-Capon and F. Coenen. Exploiting isomorphism: development of a kbs to support british coal insurance claims. In M. Sergot, editor, Proceedings of the third International Conference on AI and Law, pages 62–69, New York, 1991. ACM.Google Scholar
- [Bredeweg, 1992]B. Bredeweg. Expertise in qualitative prediction of behaviour. PhD thesis, University of Amsterdam, Amsterdam, March 1992.Google Scholar
- [Breuker & denHaan, 1991]J.A. Breuker and N den Haan. Separating regulation from world knowledge: where is the logic. In M. Sergot, editor, Proceedings of the third International Conference on AI and Law, pages 41–51, New York, NJ, 1991. ACM.Google Scholar
- [Breuker & Wielinga, 1989]J. A. Breuker and B. J. Wielinga. Model Driven Knowledge Acquisition. In P. Guida and G. Tasso, editors, Topics in the Design of Expert Systems, pages 265–296, Amsterdam, 1989. North Holland.Google Scholar
- [Breuker, 1992]J.A. Breuker. On Legal Information Serving. In C.A. Grutters, J.A. Breuker, H.J. van den Herik, A.H.J. Schmidt, and C.N.J. de Vey Mestdagh, editors, Legal Knowledge Based Systems: information technology and law, pages 93–102. Koninklijke Vermande, 1992.Google Scholar
- [Breuker et al., 1987]J. Breuker, B. Wielinga, M. van Someren, R. de Hoog, G. Schreiber, P. de Greef, B. Bredeweg, J. Wielemaker, J-P Billault, M. Davoodi, and S. Hayward. Model Driven Knowledge Acquisition: Interpretation Models. ESPRIT Project P1098 Deliverable D1 (task A1), University of Amsterdam and STL Ltd, 1987.Google Scholar
- [Clancey, 1992]W.J. Clancey. Model construction operators. Artificial Intelligence, 53:1–115, 1992.Google Scholar
- [denHaan, 1992]Nienke den Haan. TRACS: a support tool for drafting and testing law. In C.A. Grutters, J.A. Breuker, H.J. van den Herik, A.H.J. Schmidt, and C.N.J. de Vey Mestdagh, editors, Legal Knowledge Based Systems: information technology and law, pages 7–18. Koninklijke Vermande, 1992.Google Scholar
- [denHaan, 1993]Nienke den Haan. Investigations into the application of deontic logic. In R. Owens, editor, Proceedings of the IJCAI-93 Workshop on Executable and Modal Logics, page 20 p., 1993.Google Scholar
- [Jones & Sergot, 1992]A.J. Jones and M. Sergot. Deontic logic in the representation of law: towards a methodology. Artificial Intelligence and Law, 1:45–64, 1992.Google Scholar
- [McCarty, 1989]L.T. McCarty. A Language for Legal Discourse I: basic structures. In Proceedings of the 2nd International Conference on AI and Law, Vancouver, 1989. ACM.Google Scholar
- [Valente & Breuker, 1992]A. Valente and J.A. Breuker. A model based approach to legal knowledge engineering. In C.A. Grutters, J.A. Breuker, H.J. van den Herik, A.H. Schmidt, and C.N. de Vey Mestdagh, editors, Legal Knowledge Based Systems: information technology and law, pages 135–146. Koninklijke Vermande, 1992.Google Scholar
- [Valente & Breuker, 1993a]A. Valente and J.A. Breuker. A formal theory of Law. In Submitted to: Fourth conference on AI and Law. ACM, 1993.Google Scholar
- [Valente & Breuker, 1993b]A. Valente and J.A. Breuker. A functional ontology of Law. In Submitted to: Fourth conference on AI and Law. ACM, 1993.Google Scholar
- [Valente & Löckenhoff, 1993]A. Valente and Ch. Löckenhoff. Structure as guidance: a library of assessment models. In Proceedings of the EKAW-93, pages-. University of Toulouse, 1993.Google Scholar