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James Popple, A Pragmatic Legal Expert System. Applied Legal Philosophy Series

James Popple, A Pragmatic Legal Expert System. Applied Legal Philosophy Series 68 BOOK REVIEW “Expert systems” became the latest AI rage in the early 1980s, and it occurred to me that the ideas developed earlier could be used to build a case-based expert system. The basic prediction model was combined with a method of producing “reasons for decision”, and FINDER was presented at the First International Con- ference on Artificial Intelligence in Law in Boston in 1987: (Tyree et al., 1988); see also (Tyree et al., 1989). As an expert system, FINDER was very peculiar. The only input from the legal “expert” was the selection of case properties and the cases to go into the data base. This was in stark contrast to the prevailing model of a “knowledge engineer” work- ing closely with the “domain expert” to produce the knowledge content (usually production rules) of the system. FINDER was peculiar also in that its “inference engine” was based on a statistical model rather than a logical one. 2. Jurisprudence Popple devotes the first chapter and a good portion of the second to a discussion of jurisprudence and its relevance to expert systems. This is necessary, no doubt, but in the context of a PhD thesis it must have taken http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence and Law Springer Journals

James Popple, A Pragmatic Legal Expert System. Applied Legal Philosophy Series

Tyree, Alan
Artificial Intelligence and Law , Volume 8 (1) – Oct 3, 2004
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References (10)

Publisher
Springer Journals
Copyright
Copyright © 2000 by Kluwer Academic Publishers
Subject
Computer Science; Artificial Intelligence (incl. Robotics); International IT and Media Law, Intellectual Property Law; Philosophy of Law; Legal Aspects of Computing; Information Storage and Retrieval
ISSN
0924-8463
eISSN
1572-8382
DOI
10.1023/A:1008342900169
Publisher site
See Article on Publisher Site

Abstract

68 BOOK REVIEW “Expert systems” became the latest AI rage in the early 1980s, and it occurred to me that the ideas developed earlier could be used to build a case-based expert system. The basic prediction model was combined with a method of producing “reasons for decision”, and FINDER was presented at the First International Con- ference on Artificial Intelligence in Law in Boston in 1987: (Tyree et al., 1988); see also (Tyree et al., 1989). As an expert system, FINDER was very peculiar. The only input from the legal “expert” was the selection of case properties and the cases to go into the data base. This was in stark contrast to the prevailing model of a “knowledge engineer” work- ing closely with the “domain expert” to produce the knowledge content (usually production rules) of the system. FINDER was peculiar also in that its “inference engine” was based on a statistical model rather than a logical one. 2. Jurisprudence Popple devotes the first chapter and a good portion of the second to a discussion of jurisprudence and its relevance to expert systems. This is necessary, no doubt, but in the context of a PhD thesis it must have taken

Journal

Artificial Intelligence and Law – Springer Journals

Published: Oct 3, 2004

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