Lessons rom implementing factors with magnitude

We discuss the lessons learned from implementing a CATO style system using factors with magnitude. In particular we identify that giving factors magnitudes enables a diversity of reasoning styles and arguments. We distinguish a variety of ways in which factors combine to determine abstract factors. We discuss several different roles for values. Finally we identify the additional value related information required to produce a working program: thresholds and weights as well as a simple preference ordering

A formal analysis of some factor- and precedent-based accounts of precedential constraint

In this paper several recent factor- and dimension-based models of precedential constraint are formally investigated and an alternative dimension-based model is proposed. Simple factor- and dimension-based syntactic criteria are identified for checking whether a decision in a new case is forced, in terms of the relevant differences between a precedent and a new case, and the difference between absence of factors and negated factors in factor-based models is investigated. Then Horty’s and Rigoni’s recent dimension-based models of precedential constraint are critically examined. An alternative to their reason models is proposed which is less expressive but arguably easier to apply in practice

Realising angelic designs using logiak

ANGELIC is a methodology for encapsulating knowledge of a body of case law. Logiak is a system intended to support the development of logic programs by domain experts, and provides an excellent environment for the rapid realisation of ANGELIC designs. We report our use of Logiak to realise ANGELIC designs, using both Boolean factors and factors with magnitude

Thirty years of Artificial Intelligence and Law:the second decade

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments

Implementing a Theory of a Legal Domain

We describe a system for constructing, evaluating and visualising arguments based on a theory of a legal domain, developed using the Angelic methodology and the Carneades argumentation system. The visualisation can be used to explain particular cases and to refine and maintain the theory. A full implementation of the well known US Trade Secrets Domain is used to illustrate the process.</jats:p