Generating Legal Arguments
Knowledge-Based Systems, Volume 2, Issue 1, Pages 46-51, March 1989
11 Pages Posted: 18 Jul 2017
Date Written: March 1, 1989
This article is about one aspect of the DataLex Project, the FINDER application of case-based reasoning to dispute which arises when a person finds an object which is then claimed by another party. The law which governs such disputes is contained almost entirely in the decided cases (the 'finders cases').
The reasoning model used in the FINDER system has been incorporated as a separate reasoning model in a more general legal expert system shell called XSH. XSH supports the usual procedure and rule-based reasoning, but also allows for case-based legal reasoning on the FINDER model. The module is called PANNDA, an acronym for Precedent Analysis by Nearest Neighbour Discriminant Analysis. Cases are represented in PANNDA by means of frames. In the FINDER implementation, the frames are ‘flat’, i.e. they are vectors. The vector slots represent the legal facts of the case which were determined by experts in the area to be of legal significance.
FINDER calculates a measure of similarity between the client user’s case and all other cases in the knowledge base. Although a number of different algorithms have been used in FINDER, the current one is the original and performs as well as any. A weighted Euclidean distance is used as the basic measure of dissimilarity. The weights could be supplied by the expert, but if not they are generated automatically by weighting each variable an amount inversely proportional to the variance of that variance across the knowledge base. The reasons for this approach are explained.
Once the distances are calculated, FINDER searches the knowledge base for that case which is ‘closest’ to the user described case. The system then predicts that the outcome of the users case will be the same as that of the nearest neighbour.
FINDER's report subsystem is relatively simple, but it produces legal opinions which are in a form which is recognizable to lawyers. The generated opinion follows a rigid format which is described.
Various ways of using the system to test the position of cases in a set of relevant cases are described.
Keywords: Legal Expert Systems, AI and Law, Artificial Intelligence, Australia, DataLex
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