Explanation in Bayesian belief networks
Explanation in Bayesian belief networks
A probabilistic framework for explanation
A probabilistic framework for explanation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
Towards qualitative approaches to Bayesian evidential reasoning
Proceedings of the 11th international conference on Artificial intelligence and law
Argument diagramming in logic, law and artificial intelligence
The Knowledge Engineering Review
Argumentation Methods for Artificial Intelligence in Law
Argumentation Methods for Artificial Intelligence in Law
Probabilistic Semantics for the Carneades Argument Model Using Bayesian Networks
Proceedings of the 2010 conference on Computational Models of Argument: Proceedings of COMMA 2010
A hybrid formal theory of arguments, stories and criminal evidence
Artificial Intelligence and Law
Compositional Bayesian modelling for computation of evidence collection strategies
Applied Intelligence
Foundations of Bayesianism
Modeling crime scenarios in a Bayesian network
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
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Bayesian networks are a predominant approach to analyse the findings of forensic scientists. In part, this is due to the way the Bayesian approach fits the scientific method employed in forensic practice. The design of Bayesian networks that accurately and comprehensively represent the relationships between investigative hypotheses and evidence remains difficult and sometimes contentious, however. Recent research has shown that argumentation can inform the construction of Bayesian networks. But argumentation is a distinct approach to evidential reasoning with its on representation formalisms. This issue could be alleviated if it were easy to represent Bayesian networks as argumentation diagrams. This position paper presents an investigation into the similarities, differences and synergies between Bayesian networks and argumentation diagrams and shows a first version of an algorithm to extract argumentation diagrams from Bayesian networks.