David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Artificial Intelligence and Law 3 (4):221-265 (1995)
A representation methodology for knowledge allowing multiple interpretations is described. It is based on the following conception of legal knowledge and its open texture. Since indeterminate, legal knowledge must be adapted to fit the circumstances of the cases to which it is applied. Whether a certain adaptation is lawful or not is measured by metaknowledge. But as this too is indeterminate, its adaptation to the case must be measured by metametaknowledge, etc. This hierarchical model of law is quite well-established and may serve well as a basis for a legal knowledge system. To account for the indeterminacy of law such a system should support the construction of different arguments for and against various interpretations of legal sources. However, automatizing this reasoning fully is unsound since it would imply a restriction to arguments defending interpretations anticipated at programming time. Therefore, the system must be interactive and the user''s knowledge be furnished in a principled way. Contrary to the widespread opinion that classical logic is inadequate for representing open-textured knowledge, the framework outlined herein is given a formalization in first order logic.
|Keywords||multiple interpretation open texture vagueness schemata metalogic programming metalogic knowledge representation|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
Trevor Bench-Capon & Henry Prakken (2010). Using Argument Schemes for Hypothetical Reasoning in Law. Artificial Intelligence and Law 18 (2):153-174.
Similar books and articles
Joost Breuker, André Valente & Radboud Winkels (2004). Legal Ontologies in Knowledge Engineering and Information Management. Artificial Intelligence and Law 12 (4):241-277.
Pauline Westerman (2010). Arguing About Goals: The Diminishing Scope of Legal Reasoning. [REVIEW] Argumentation 24 (2):211-226.
Adel Saadoun, Jean-Louis Ermine, Claude Belair & Jean-Mark Pouyot (1997). A Knowledge Engineering Framework for Intelligent Retrieval of Legal Case Studies. Artificial Intelligence and Law 5 (3):179-205.
V. R. Benjamins, J. Contreras, P. Casanovas, M. Ayuso, M. Becue, L. Lemus & C. Urios (2004). Ontologies of Professional Legal Knowledge as the Basis for Intelligent IT Support for Judges. Artificial Intelligence and Law 12 (4):359-378.
Graham Greenleaf, Andrew Mowbray & Peter Dijk (1995). Representing and Using Legal Knowledge in Integrated Decision Support Systems: Datalex Workstations. [REVIEW] Artificial Intelligence and Law 3 (1-2):97-142.
José Saias & Paulo Quaresma (2004). A Methodology to Create Legal Ontologies in a Logic Programming Based Web Information Retrieval System. Artificial Intelligence and Law 12 (4):397-417.
T. J. M. Bench-Capon & F. P. Coenen (1992). Isomorphism and Legal Knowledge Based Systems. Artificial Intelligence and Law 1 (1):65-86.
Stefania Costantini & Gaetano Aurelio Lanzarone (1995). Explanation-Based Interpretation of Open-Textured Concepts in Logical Models of Legislation. Artificial Intelligence and Law 3 (3):191-208.
Anja Oskamp (1992). Model for Knowledge and Legal Expert Systems. Artificial Intelligence and Law 1 (4):245-274.
Added to index2009-01-28
Total downloads13 ( #142,452 of 1,692,923 )
Recent downloads (6 months)1 ( #193,926 of 1,692,923 )
How can I increase my downloads?