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  1. Ruth Kannai, Uri Schild & John Zeleznikow (2007). Modeling the Evolution of Legal Discretion. An Artificial Intelligence Approach. Ratio Juris 20 (4):530-558.
  2. Giles Oatley, Brian Ewart & John Zeleznikow (2006). Decision Support Systems for Police: Lessons From the Application of Data Mining Techniques to “Soft” Forensic Evidence. [REVIEW] Artificial Intelligence and Law 14 (1-2):35-100.
    The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors’ work with three Police Services. The focus is upon the use of “soft” forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than “hard” evidence such (...)
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  3. John Zeleznikow (2006). Book Review. [REVIEW] Artificial Intelligence and Law 14 (3):247-248.
  4. Emilia Bellucci & John Zeleznikow (2005). Developing Negotiation Decision Support Systems That Support Mediators: A Case Study of the Family_winner System. [REVIEW] Artificial Intelligence and Law 13 (2):233-271.
    Negotiation Support Systems have traditionally modelled the process of negotiation. They often rely on mathematical optimisation techniques and ignore heuristics and other methods derived from practice. Our goal is to develop systems capable of decision support to help resolve a given dispute. A system we have constructed, Family_Winner, uses empirical evidence to dynamically modify initial preferences throughout the negotiation process. It sequentially allocates issues using trade-offs and compensation opportunities inherent in the dispute.
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  5. Arno R. Lodder & John Zeleznikow (2005). Preface. Artificial Intelligence and Law 13 (2):189-192.
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  6. Edwina Rissland, Kevin Ashley, Marc Lauritsen, Patricia Hassett, Jc Smith, John Zeleznikow, Andrew Stranieri, Dan Hunter & George Vossos (2002). Special Issue in Memory of Donald H. Berman. Artificial Intelligence and Law 10:309-310.
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  7. John Zeleznikow (2002). An Australian Perspective on Research and Development Required for the Construction of Applied Legal Decision Support Systems. Artificial Intelligence and Law 10 (4):237-260.
    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 (...)
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  8. Andrew Stranieri, John Zeleznikow, Mark Gawler & Bryn Lewis (1999). A Hybrid Rule – Neural Approach for the Automation of Legal Reasoning in the Discretionary Domain of Family Law in Australia. Artificial Intelligence and Law 7 (2-3):153-183.
    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 (...)
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  9. John Zeleznikow, Andrew Stranieri & Mark Gawler (1995). Project Report: Split-Up — a Legal Expert System Which Determines Property Division Upon Divorce. [REVIEW] Artificial Intelligence and Law 3 (4):267-275.
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  10. John Zeleznikow, George Vossos & Daniel Hunter (1993). The IKBALS Project: Multi-Modal Reasoning in Legal Knowledge Based Systems. [REVIEW] Artificial Intelligence and Law 2 (3):169-203.
    In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval.When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based (...)
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