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A computational framework for conceptual blending

AutorEppe, Manfred; Maclean, Ewen; Confalonieri, Roberto; Kutz, Oliver; Schorlemmer, Marco ; Plaza, Enric ; Kühnberger, Kai U.
Palabras claveConceptual blending
Computational creativity
Cognitive science
Answer set programming
Fecha de publicación2018
EditorElsevier
CitaciónArtificial Intelligence 256: 105- 129 (2018)
ResumenWe 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. © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
URIhttp://hdl.handle.net/10261/161339
Identificadoresdoi: 10.1016/j.artint.2017.11.005
issn: 0004-3702
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