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Modeling Creativity: Case Studies in Python

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Abstract

Modeling Creativity (doctoral dissertation, 2013) explores how creativity can be represented using computational approaches. Our aim is to construct computer models that exhibit creativity in an artistic context, that is, that are capable of generating or evaluating an artwork (visual or linguistic), an interesting new idea, a subjective opinion. The research was conducted in 2008-2012 at the Computational Linguistics Research Group (CLiPS, University of Antwerp) under the supervision of Prof. Walter Daelemans. Prior research was also conducted at the Experimental Media Research Group (EMRG, St. Lucas University College of Art & Design Antwerp) under the supervision of Lucas Nijs. Modeling Creativity examines creativity in a number of different perspectives: from its origins in nature, which is essentially blind, to humans and machines, and from generating creative ideas to evaluating and learning their novelty and usefulness. We will use a hands-on approach with case studies and examples in the Python programming language.

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... This semantic graph contains 32 relations between 32 attributes of the animals horse and dragon, such as physical parts, health resistance and some taxonomic properties (Fig. 4b). The last is the Perception semantic graph from [9]. Afterwards, we proceeded with the experiments on the three semantic graphs. ...
... In order to study our algorithm with a more complex and practical problem, we researched the Perception [9] knowledge base with two experiments. For the rst, we did a study regarding the eect of τ in the size of the two sub-graphs. ...
... Por otra parte, centrándonos en la librería Textblob, y entender el funcionamiento es de suma importancia. La librería contiene una biblioteca de patrones [68], la cual contiene una gran cantidad de palabras, esta biblioteca está basada en los trabajos de Tom De Smedt y Walter Daelemans [69], como se muestra a continuación. ...
Thesis
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... Being a distinctive feature might carry certain skepticism when attributed to other entities, but building machines that exhibit this capability certainly represents a challenge for scientist and engineers. From the point of view of computer science, this is a cutting-edge field in computational intelligence, usually addressing complex and vague problems, with ill-defined spaces of search (Gero & Maher, 1993) (De Smedt, 2013), so that using conventional algorithms is not feasible here. ...
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... Finally, this work is related to the broad area of computational creativity [30,7], which focuses on developing artificial intelligence models that exhibit or generate creativity (e.g., problem solving [23], visual creativity [5], and linguistic creativity [27]). In contrast, this work focuses on understanding and modeling creative processes in scientific enterprise. ...
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... One possibility is to use an already built semantic network of common sense knowledge (e.g. [8] or [3]). ...
Conference Paper
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