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Introduction: from legal theories to neural networks and fuzzy reasoning

Introduction: from legal theories to neural networks and fuzzy reasoning Computational approaches to the law have frequently been characterized as being formalistic implementations of the syllogistic model of legal cognition: using insufficient or contradictory data, making analogies, learning through examples and experiences, applying vague and imprecise standards. We argue that, on the contrary, studies on neural networks and fuzzy reasoning show how AI & law research can go beyond syllogism, and, in doing that, can provide substantial contributions to the law. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence and Law Springer Journals

Introduction: from legal theories to neural networks and fuzzy reasoning

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Publisher
Springer Journals
Copyright
Copyright © 1999 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:1008371600675
Publisher site
See Article on Publisher Site

Abstract

Computational approaches to the law have frequently been characterized as being formalistic implementations of the syllogistic model of legal cognition: using insufficient or contradictory data, making analogies, learning through examples and experiences, applying vague and imprecise standards. We argue that, on the contrary, studies on neural networks and fuzzy reasoning show how AI & law research can go beyond syllogism, and, in doing that, can provide substantial contributions to the law.

Journal

Artificial Intelligence and LawSpringer Journals

Published: Sep 30, 2004

References