A computational framework for conceptual blending
Citation Link: https://doi.org/10.15480/882.4135
Publikationstyp
Journal Article
Date Issued
2017-12-02
Sprache
English
TORE-DOI
Journal
Volume
256
Start Page
105
End Page
129
Citation
Artificial Intelligence 256 : 105-129 (2018-03)
Publisher DOI
Scopus ID
Publisher
Elsevier
We present a computational framework for conceptual blending, a concept invention method that is advocated in cognitive science as a fundamental and uniquely human engine for creative thinking. Our framework treats a crucial part of the blending process, namely the generalisation of input concepts, as a search problem that is solved by means of modern answer set programming methods to find commonalities among input concepts. We also address the problem of pruning the space of possible blends by introducing metrics that capture most of the so-called optimality principles, described in the cognitive science literature as guidelines to produce meaningful and serendipitous blends. As a proof of concept, we demonstrate how our system invents novel concepts and theories in domains where creativity is crucial, namely mathematics and music.
Subjects
Answer set programming
Cognitive science
Computational creativity
Conceptual blending
DDC Class
004: Informatik
Funding Organisations
More Funding Information
The research presented in this article was partially supported by the COINVENT project (FET-Open grant number: 611553). Manfred Eppe received support by the German Academic Exchange Service (DAAD) as participant in the FITweltweit programme. Oliver Kutz and Roberto Confalonieri were supported by the unibz CRC project COCO “Computational Technologies for Con-cept Invention”.
Publication version
publishedVersion