A central theme throughout the impressive series of philosophical books and articles Stephen Toulmin has published since 1948 is the way in which assertions and opinions concerning all sorts of topics, brought up in everyday life or in academic research, can be rationally justified. Is there one universal system of norms, by which all sorts of arguments in all sorts of fields must be judged, or must each sort of argument be judged according to its own norms? In The Uses (...) of Argument Toulmin sets out his views on these questions for the first time. In spite of initial criticisms from logicians and fellow philosophers, The Uses of Argument has been an enduring source of inspiration and discussion to students of argumentation from all kinds of disciplinary background for more than forty years. (shrink)

Few automated legal reasoning systems have been developed in domains of law in which a judicial decision maker has extensive discretion in the exercise of his or her powers. Discretionary domains challenge existing artificial intelligence paradigms because models of judicial reasoning are difficult, if not impossible to specify. We argue that judicial discretion adds to the characterisation of law as open textured in a way which has not been addressed by artificial intelligence and law researchers in depth. We demonstrate that (...) systems for reasoning with this form of open texture can be built by integrating rule sets with neural networks trained with data collected from standard past cases. The obstacles to this approach include difficulties in generating explanations once conclusions have been inferred, difficulties associated with the collection of sufficient data from past cases and difficulties associated with integrating two vastly different paradigms. A knowledge representation scheme based on the structure of arguments proposed by Toulmin has been used to overcome these obstacles. The system, known as Split Up, predicts judicial decisions in property proceedings within family law in Australia. Predictions from the system have been compared to those from a group of lawyers with favourable results. (shrink)

Cutting a cake, dividing up the property in an estate, determining the borders in an international dispute - such problems of fair division are ubiquitous. Fair Division treats all these problems and many more through a rigorous analysis of a variety of procedures for allocating goods, or deciding who wins on what issues, when there are disputes. Starting with an analysis of the well-known cake-cutting procedure, 'I cut, you choose', the authors show how it has been adapted in a number (...) of fields and then analyze fair-division procedures applicable to situations in which there are more than two parties, or there is more than one good to be divided. In particular they focus on procedures which provide 'envy-free' allocations, in which everybody thinks he or she has received the largest portion and hence does not envy anybody else. They also discuss the fairness of different auction and election procedures. (shrink)

Our goal is to model reasoning in discretionary legal domains. To do so, we use Knowledge Discovery from Database Techniques. However there are obstacles to this approach, including difficulties in generating explanations once conclusions have been inferred, difficulties associated with the collection of sufficient data from past cases and difficulties associated with integrating two vastly different paradigms. Toulmin’s treatise on the uses of argument can be gainfully employed to construct legal decision support systems in discretionary domains. We show how we (...) can use Toulmin’s approach to build such systems with examples taken from the domains of eligibility for legal aid, evaluation of eyewitness evidence, family law, refugee law and sentencing. We then show how Toulmin Argument Structures can be developed to construct an Online Dispute Resolution environment that allows for determining BATNAs, exchanging opinions and providing advice about tradeoffs. (shrink)