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  1. Formalizing multiple interpretation of legal knowledge.Andreas Hamfelt - 1995 - Artificial Intelligence and Law 3 (4):221-265.
    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 (...)
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  • On isomorphic formalisations.Routen Tom - 1996 - Artificial Intelligence and Law 4 (2):113-132.
    Previous research into the formalisation of statute law identified a number of uses of language which posed problems for formalisation. A previous paper argued that these uses establish the requirement that a formalisation be isomorphic, but noted that this has odd consequences. This paper expands on what these consequences are and argues that they undermine the very idea of formalisation. Therefore, the whole argument constitutes a reductio ad absurdum of the idea of formalising statute law. The paper provides reasons why (...)
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  • Modular argumentation for modelling legal doctrines in common law of contract.Phan Minh Dung & Phan Minh Thang - 2009 - Artificial Intelligence and Law 17 (3):167-182.
    To create a programming environment for contract dispute resolution, we propose an extension of assumption-based argumentation into modular assumption-based argumentation in which different modules of argumentation representing different knowledge bases for reasoning about beliefs and facts and for representation and reasoning with the legal doctrines could be built and assembled together. A distinct novel feature of modular argumentation in compare with other modular logic-based systems like Prolog is that it allows references to different semantics in the same module at the (...)
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  • Explanation-based interpretation of open-textured concepts in logical models of legislation.Stefania Costantini & Gaetano Aurelio Lanzarone - 1995 - Artificial Intelligence and Law 3 (3):191-208.
    In this paper we discuss a view of the Machine Learning technique called Explanation-Based Learning (EBL) or Explanation-Based Generalization (EBG) as a process for the interpretation of vague concepts in logic-based models of law.The open-textured nature of legal terms is a well-known open problem in the building of knowledge-based legal systems. EBG is a technique which creates generalizations of given examples on the basis of background domain knowledge. We relate these two topics by considering EBG''s domain knowledge as corresponding to (...)
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  • Law making environment: model based system for the formulation, research and diagnosis of legislation.Carlo Biagioli - forthcoming - Artificial Intelligence and Law.
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