Representing the structure of a legal argument
ICAIL '89 Proceedings of the 2nd international conference on Artificial intelligence and law
The pleadings game: formalizing procedural justice
ICAIL '93 Proceedings of the 4th international conference on Artificial intelligence and law
Types of expertise: an invariant of problem solving
International Journal of Man-Machine Studies
Participating in explanatory dialogues: interpreting and responding to questions in context
Participating in explanatory dialogues: interpreting and responding to questions in context
Burden of proof in legal argumentation
ICAIL '95 Proceedings of the 5th international conference on Artificial intelligence and law
Knowledge discovery in the Split Up project
Proceedings of the 6th international conference on Artificial intelligence and law
Scaling of neural network inferencing by efficient storage and retrieval of outputs
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Using Cases to Build Intelligent Decision Support Systems
DS-6 Proceedings of the Sixth IFIP TC-2 Working Conference on Data Semantics: Database Applications Semantics
A conceptual, case-relation representation of text for intelligent retrieval
A conceptual, case-relation representation of text for intelligent retrieval
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Marco Somalvico Memorial Issue
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Split Up is a rule / neural hybrid that represents knowledge using frames based on the argument structure proposed by the British philosopher, Toulmin. Split Up makes predictions about marital property following a divorce in Australia; a domain that is considered discretionary in that a judge has considerable flexibility. The end users of Split Up are judges and registrars of the Family Court of Australia, mediators and lawyers. Each end user has specific and divergent needs and thus uses the system in different ways however all users rely on effective explanations. The argument based representation of knowledge enables the system to have the flexibility required of different users, to generate effective explanations and also facilitates knowledge acquisition. The framework has been used to integrate rules with neural networks but can easily be used to integrate other inferencing methods.