Content Determination for Natural Language Descriptions of Predictive Bayesian Networks

DOI

10.2991/eusflat-19.2019.107How to use a DOI?

Keywords

content determination in natural language generation linguistic descriptions fuzzy syllogism

Abstract

The dramatic success of Artificial Intelligence applications has been accompanied by more complexity, which makes its comprehension for final users more difficult and damages trustworthiness as a result. Within this context, the emergence of Explainable AI aims to make intelligent systems decisions and internal processes more comprehensible to human users. In this paper, we propose a framework for the explanation in natural language of predictive inference in Bayesian Networks (BN) to non-specialized users. The model uses a fuzzy syllogistic model for building a knowledge base made up of binary quantified statements that make explicit in a linguistic way all the relevant information which is implicit in a BN approximate reasoning model. Through a number of examples, it is shown how the generated explanations allow the user to trace the inference steps in the approximate reasoning process in predictive Bayesian Networks.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Martín Pereira-Fariña
AU  - Alberto Bugarín
PY  - 2019/08
DA  - 2019/08
TI  - Content Determination for Natural Language Descriptions of Predictive Bayesian Networks
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 784
EP  - 791
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.107
DO  - 10.2991/eusflat-19.2019.107
ID  - Pereira-Fariña2019/08
ER  -