Symbolic Explanation Module for Fuzzy Cognitive Map-Based Reasoning Models

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

*Corresponding author for this work

    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
    Country/TerritoryUnited Kingdom
    CityCambridge
    Period15/12/2017/12/20
    Internet address

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