When we talk about the coherence of a story, we seem to think of how well its individual pieces fit together—how to explicate this notion formally, though? We develop a Bayesian network based coherence measure with implementation in _R_, which performs better than its purely probabilistic predecessors. The novelty is that by paying attention to the network structure, we avoid simply taking mean confirmation scores between all possible pairs of subsets of a narration. Moreover, we assign special importance to the (...) weakest links in a narration, to improve on the other measures’ results for logically inconsistent scenarios. We illustrate and investigate the performance of the measures in relation to a few philosophically motivated examples, and (more extensively) using the real-life example of the Sally Clark case. (shrink)

Fenton et al. present a Bayesian‐network analysis of the case, using their previously developed set of building blocks (‘idioms’). They claim that these idioms, combined with their opportunity‐based method for estimating the prior probability of guilt, reduce the subjectivity of their analysis. Although their Bayesian model is less cognitively feasible than scenario‐ or argumentation‐based models, they claim that it does model the standard approach to legal proof, which is to continually revise beliefs under new evidence.

The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This paper discusses several topics that relate more naturally to groups of papers than a single paper published in the journal: ontologies, reasoning about evidence, the various contributions of Douglas Walton, and the practical application of the techniques of AI and Law.