Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Probability elicitation for belief networks: issues to consider
The Knowledge Engineering Review
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
A hybrid formal theory of arguments, stories and criminal evidence
Artificial Intelligence and Law
On extracting arguments from Bayesian network representations of evidential reasoning
Proceedings of the 13th International Conference on Artificial Intelligence and Law
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Legal cases involve reasoning with evidence and with the development of a software support tool in mind, a formal foundation for evidential reasoning is required. Three approaches to evidential reasoning have been prominent in the literature: argumentation, narrative and probabilistic reasoning. In this paper a combination of the latter two is proposed. In recent research on Bayesian networks applied to legal cases, a number of legal idioms have been developed as recurring structures in legal Bayesian networks. A Bayesian network quantifies how various variables in a case interact. In the narrative approach, scenarios provide a context for the evidence in a case. A method that integrates the quantitative, numerical techniques of Bayesian networks with the qualitative, holistic approach of scenarios is lacking. In this paper, a method is proposed for modeling several scenarios in a single Bayesian network. The method is tested by doing a case study. Two new idioms are introduced: the scenario idiom and the merged scenarios idiom. The resulting network is meant to assist a judge or jury, helping to maintain a good overview of the interactions between relevant variables in a case and preventing tunnel vision by comparing various scenarios.