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Computational Inference for Evidential Reasoning in Support of Judicial Proof

  • Tod S. Levitt
  • Kathryn Blackmond Laskey
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 94)

Abstract

The process of judicial proof accrues evidence to confirm or deny hypotheses about world events relevant to a legal case. Software applications that seek to support this process must provide the user with sophisticated capabilities to manipulate evidential reasoning for legal cases. This requires computational techniques to represent the actors, entities, events, and context of world situations to structure alternative hypotheses interpreting evidence and to execute processes that draw inferences about the truth of hypotheses by assessing the relevance and weight of evidence to confirm or deny the hypotheses. Bayesian inference networks are combined with knowledge representations from artificial intelligence to structure and analyze evidential argumentation. The infamous 1994 Raddad murder trial in Nice, France provides a backdrop against which we illustrate the application of these techniques to evidential reasoning in support of judicial proof.

Keywords

Bayesian Network Supra Note Argument Structure Evidential Reasoning Legal Case 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Physica-Verlag Heidelberg 2002

Authors and Affiliations

  • Tod S. Levitt
  • Kathryn Blackmond Laskey

There are no affiliations available

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