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  1. Argument diagram extraction from evidential Bayesian networks.Jeroen Keppens - 2012 - Artificial Intelligence and Law 20 (2):109-143.
    Bayesian networks (BN) and argumentation diagrams (AD) are two predominant approaches to legal evidential reasoning, that are often treated as alternatives to one another. This paper argues that they are, instead, complimentary and proposes the beginnings of a method to employ them in such a manner. The Bayesian approach tends to be used as a means to analyse the findings of forensic scientists. As such, it constitutes a means to perform evidential reasoning. The design of Bayesian networks that accurately and (...)
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  • Building Bayesian networks for legal evidence with narratives: a case study evaluation.Charlotte S. Vlek, Henry Prakken, Silja Renooij & Bart Verheij - 2014 - Artificial Intelligence and Law 22 (4):375-421.
    In a criminal trial, evidence is used to draw conclusions about what happened concerning a supposed crime. Traditionally, the three main approaches to modeling reasoning with evidence are argumentative, narrative and probabilistic approaches. Integrating these three approaches could arguably enhance the communication between an expert and a judge or jury. In previous work, techniques were proposed to represent narratives in a Bayesian network and to use narratives as a basis for systematizing the construction of a Bayesian network for a legal (...)
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  • Arguments, stories and criminal evidence: A formal hybrid theory.Floris J. Bex - 2011 - Springer.
    In this book a theory of reasoning with evidence in the context of criminal cases is developed. The main subject of this study is not the law of evidence but rather the rational process of proof, which involves constructing, testing and justifying scenarios about what happened using evidence and commonsense knowledge. A central theme in the book is the analysis of ones reasoning, so that complex patterns are made more explicit and clear. This analysis uses stories about what happened and (...)
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