.png){kind=icon}
.png){kind=icon}
Return
A two-phase method for extracting explanatory arguments from Bayesian networks
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijar.2016.09.002.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Errors in reasoning about probabilistic evidence can have severe consequences. In the legal domain a number of recent miscarriages of justice emphasises how severe these consequences can be. These cases, in which forensic evidence was misinterpreted, have ignited a scientific debate on how and when probabilistic reasoning can be incorporated in (legal) argumentation. One promising approach is to use Bayesian networks (BNs), which are well-known scientific models for probabilistic reasoning. For non-statistical experts, however, Bayesian networks may be hard to interpret. Especially since the inner workings of Bayesian networks are complicated, they may appear as black box models. Argumentation models, on the contrary, can be used to show how certain results are derived in a way that naturally corresponds to everyday reasoning. In this paper we propose to explain the inner workings of a BN in terms of arguments. We formalise a two-phase method for extracting probabilistically supported arguments from a Bayesian network. First, from a Bayesian network we construct a support graph, and, second, given a set of observations we build arguments from that support graph. Such arguments can facilitate the correct interpretation and explanation of the relation between hypotheses and evidence that is modelled in the Bayesian network. (C) 2016 Elsevier Inc. All rights reserved.
Keywords:
Bayesian networks
Argumentation
Probabilistic reasoning
Explanation
Inference
Uncertainty
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
2.9K
Citations:
5.1K