Abstract
In order to make an informed decision in a criminal trial, conclusions
about what may have happened need to be derived from the available evidence.
Recently, Bayesian networks have gained popularity as a probabilistic tool for rea-
soning with evidence. However, in order to make sense of a conclusion drawn
from a Bayesian network, a juror needs to understand the context. In this paper,
we propose to extract scenarios from a Bayesian network to form the context for
the results of computations in that network. We interpret the narrative concepts of
scenario schemes, local coherence and global coherence in terms of probabilities.
These allow us to present an algorithm that takes the most probable configuration
of variables of interest, computed from the Bayesian network, and forms a coher-
ent scenario as a context for these variables. This way, we take advantage of the
calculations in a Bayesian network, as well as the global perspective of narratives
about what may have happened need to be derived from the available evidence.
Recently, Bayesian networks have gained popularity as a probabilistic tool for rea-
soning with evidence. However, in order to make sense of a conclusion drawn
from a Bayesian network, a juror needs to understand the context. In this paper,
we propose to extract scenarios from a Bayesian network to form the context for
the results of computations in that network. We interpret the narrative concepts of
scenario schemes, local coherence and global coherence in terms of probabilities.
These allow us to present an algorithm that takes the most probable configuration
of variables of interest, computed from the Bayesian network, and forms a coher-
ent scenario as a context for these variables. This way, we take advantage of the
calculations in a Bayesian network, as well as the global perspective of narratives
| Original language | English |
|---|---|
| Title of host publication | Legal Knowledge and Information Systems. JURIX 2014: The Twenty-seventh Annual Conference |
| Editors | Rinke Hoekstra |
| Place of Publication | Amsterdam etc |
| Publisher | IOS Press |
| Pages | 103-112 |
| Number of pages | 10 |
| ISBN (Electronic) | 978-1-61499-468-8 |
| ISBN (Print) | 978-1-61499-467-1 |
| DOIs | |
| Publication status | Published - 2014 |
Publication series
| Name | Frontiers in Artificial Intelligence and Applications |
|---|---|
| Publisher | IOS Press |
| Volume | 271 |
| ISSN (Print) | 0922-6389 |
| ISSN (Electronic) | 1879-8314 |
