Symbolic Explanation Module for Fuzzy Cognitive Map-Based Reasoning Models

Fabian Hoitsma, Andreas Knoben, Maikel Leon Espinosa, Gonzalo Nápoles

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

In recent years, pattern classification has started to move from computing models with outstanding prediction rates to models able to reach a suitable trade-off between accuracy and interpretability. Fuzzy Cognitive Maps (FCMs) and their extensions are recurrent neural networks that have been partially exploited towards fulfilling such a goal. However, the interpretability of these neural systems has been confined to the fact that both neural concepts and weights have a well-defined meaning for the problem being modeled. This rather naive assumption oversimplifies the complexity behind an FCM-based classifier. In this paper, we propose a symbolic explanation module that allows extracting useful insights and patterns from a trained FCM-based classifier. The proposed explanation module is implemented in Prolog and can be seen as a reverse symbolic reasoning rule that infers the inputs to be provided to the model to obtain the desired output.
Original languageEnglish
Title of host publicationArtificial Intelligence XXXVII
Pages21-34
Number of pages14
Publication statusPublished - 2020
Event40th SGAI International Conference on Artificial Intelligence - Cambridge, United Kingdom
Duration: 15 Dec 202017 Dec 2020
http://bcs-sgai.org/ai2020/

Conference

Conference40th SGAI International Conference on Artificial Intelligence
CountryUnited Kingdom
CityCambridge
Period15/12/2017/12/20
Internet address

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