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Using Toulmin Argumentation to develop an Online Dispute Resolution Environment

John Zeleznikow

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Trade-off manipulations in the development of negotiation decision support systems.E. Bellucci & J. Zeleznikow - unknowndetails Our model of negotiation for constructing Negotiation Decision Support Systems is based upon Principled Negotiation and uses trade-off manipulations in order to provide decision support. A resulting system, Family_Winner, was constructed using several information systems techniques. Tradeoff Maps (a variant of Constraint Diagrams) are used to represent trade-off opportunities, while an empirically derived formula calculates the amount of compensation given to the ratings of issues remaining in dispute. The Issue Decomposition Hierarchy embedded in the system allows for the incorporation of (...) Download Export citation Bookmark 1 citation
An australian perspective on research and development required for the construction of applied legal decision support systems.John Zeleznikow - 2002 - Artificial Intelligence and Law 10 (4):237-260.details At the Donald Berman Laboratory for Information Technology and Law, La TrobeUniversity Australia, we have been building legal decision support systems for a dozenyears. Whilst most of our energy has been devoted to conducting research in ArtificialIntelligence and Law, over the past few years we have increasingly focused uponbuilding legal decision support systems that have a commercial focus.In this paper we discuss the evolution of our systems. We begin with a discussion ofrule-based systems and discuss the transition to hybrid rule-based/case-based (...) Download Export citation Bookmark 1 citation
A hybrid rule – neural approach for the automation of legal reasoning in the discretionary domain of family law in australia.Andrew Stranieri, John Zeleznikow, Mark Gawler & Bryn Lewis - 1999 - Artificial Intelligence and Law 7 (2-3):153-183.details 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 (...) Download Export citation Bookmark 16 citations
DiaLaw. On legal justification and dialogical models of argumentation.Arno R. Lodder - 1999 - Dordrecht, Boston and London: Kluwer Academic Publishers.details In this work it is argued that legal justification can best be studied from a procedural, dialogical point of view: legal statements are justified if the audience is convinced in an argumentative dialog. The formalized and implemented model DiaLaw guards the procedure in which two players aim at justifying statements. DiaLaw shows the advances and problems linked to procedural models of legal justification. Moreover, an instructive discussion of the different models of procedural justification is provided. It is stressed that in (...) Download Export citation Bookmark 19 citations
A computational model of ratio decidendi.L. Karl Branting - 1993 - Artificial Intelligence and Law 2 (1):1-31.details This paper proposes a model ofratio decidendi as a justification structure consisting of a series of reasoning steps, some of which relate abstract predicates to other abstract predicates and some of which relate abstract predicates to specific facts. This model satisfies an important set of characteristics ofratio decidendi identified from the jurisprudential literature. In particular, the model shows how the theory under which a case is decided controls its precedential effect. By contrast, a purely exemplar-based model ofratio decidendi fails to (...) Download Export citation Bookmark 18 citations

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