David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Artificial Intelligence and Law 7 (2-3):211-225 (1999)
The representation of knowledge in the law has basically followed a rule-based logical-symbolic paradigm. This paper aims to show how the modeling of legal knowledge can be re-examined using connectionist models, from the perspective of the theory of the dynamics of unstable systems and chaos. We begin by showing the nature of the paradigm shift from a rule-based approach to one based on dynamic structures and by discussing how this would translate into the field of theory of law. In order to show the full potential of this new approach, we start from an experiment with NEUROLEX, in which a neural network was used to model a corpus of French Council of State decisions. We examine the implications of this experiment, especially those concerning the limits of the model used, and show that other connectionist models might correspond more adequately to the nature of legal knowledge. Finally, we propose another neural model which could show not only the rules which emerge from legal qualification (NEUROLEX's goal), but also the way in which a legal qualification process evolves from one concept to another.
|Keywords||case law dynamic systems legal neural networks rule based model self-organisation|
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