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Reasoning paradigms in legal decision support systems

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Abstract

In this paper we discuss the strengths and weaknesses of a range of artificial intelligence approaches used in legal domains. Symbolic reasoning systems which rely on deductive, inductive and analogical reasoning are described and reviewed. The role of statistical reasoning in law is examined, and the use of neural networks analysed. There is discussion of architectures for, and examples of, systems which combine a number of these reasoning strategies. We conclude that to build intelligent legal decision support systems requires a range of reasoning strategies.

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Authors and Affiliations

  1. CLAIR Project, Database Research Laboratory, Applied Computing Research Institute, La Trobe University, 3083, Bundoora, Victoria, Australia

    John Zeleznikow

  2. CLAIR Project, Law School, University of Melbourne, 3052, Parkville, Victoria, Australia

    Dan Hunter

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  1. John Zeleznikow
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  2. Dan Hunter
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Zeleznikow, J., Hunter, D. Reasoning paradigms in legal decision support systems. Artif Intell Rev 9, 361–385 (1995). https://doi.org/10.1007/BF00849064

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  • DOI: https://doi.org/10.1007/BF00849064

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