Computational creativity: an interdisciplinary approach to sequential learning and creative generations

Barbaresi, Mattia (2023) Computational creativity: an interdisciplinary approach to sequential learning and creative generations, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Computer science and engineering, 35 Ciclo. DOI 10.48676/unibo/amsdottorato/10840.
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Creativity seems mysterious; when we experience a creative spark, it is difficult to explain how we got that idea, and we often recall notions like ``inspiration" and ``intuition" when we try to explain the phenomenon. The fact that we are clueless about how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. We are unaware of how we perform certain tasks, such as biking or language understanding, but we have more and more computational techniques that can replicate and hopefully explain such activities. We should understand that every creative act is a fruit of experience, society, and culture. Nothing comes from nothing. Novel ideas are never utterly new; they stem from representations that are already in mind. Creativity involves establishing new relations between pieces of information we had already: then, the greater the knowledge, the greater the possibility of finding uncommon connections, and the more the potential to be creative. In this vein, a beneficial approach to a better understanding of creativity must include computational or mechanistic accounts of such inner procedures and the formation of the knowledge that enables such connections. That is the aim of Computational Creativity: to develop computational systems for emulating and studying creativity. Hence, this dissertation focuses on these two related research areas: discussing computational mechanisms to generate creative artifacts and describing some implicit cognitive processes that can form the basis for creative thoughts.

Tipologia del documento
Tesi di dottorato
Barbaresi, Mattia
Dottorato di ricerca
Settore disciplinare
Settore concorsuale
Parole chiave
creativity, statistical learning, sequential learning, implicit learning, artificial intelligence
Data di discussione
5 Luglio 2023

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