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Discovering the preference hypervolume: an interactive model for real world computational co-creativity

  • In this thesis it is posed that the central object of preference discovery is a co-creative process in which the Other can be represented by a machine. It explores efficient methods to enhance introverted intuition using extraverted intuition's communication lines. Possible implementations of such processes are presented using novel algorithms that perform divergent search to feed the users' intuition with many examples of high quality solutions, allowing them to take influence interactively. The machine feeds and reflects upon human intuition, combining both what is possible and preferred. The machine model and the divergent optimization algorithms are the motor behind this co-creative process, in which machine and users co-create and interactively choose branches of an ad hoc hierarchical decomposition of the solution space. The proposed co-creative process consists of several elements: a formal model for interactive co-creative processes, evolutionary divergent search, diversity and similarity, data-driven methods to discover diversity, limitations of artificial creative agents, matters of efficiency in behavioral and morphological modeling, visualization, a connection to prototype theory, and methods to allow users to influence artificial creative agents. This thesis helps putting the human back into the design loop in generative AI and optimization.

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Document Type:Doctoral Thesis
Author:Alexander Hagg
Number of pages:201
Referee:Thomas H. W. Bäck, Alexander Asteroth
Date of exam:2021/12/07
Contributing Corporation:Leiden University
Date of first publication:2021/12/07
Copyright:Copyright © 2021 Alexander Hagg.
Funding:This work received funding from the German Federal Ministry of Education and Research, and the Ministry for Culture and Science of the state of North Rhine-Westphalia (research grants 03FH012PX5 and 13FH156IN6).
Keyword:Clustering; Co-creative processes; Computational creativity; Dimensionality reduction; Divergent optimization; Evolutionary optimization; Generative Models; Human-Computer Interaction; Quality diversity; Surrogate-assistance
Departments, institutes and facilities:Fachbereich Informatik
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Projects:AErOMAt - Automatisiertes Entwickeln aerodynamischer Strukturen und Fahrzeuge mithilfe evolutionärer Optimierung und Surrogatmodellierung (DE/BMBF/03FH012PX5,13FH012PX5)
EI-HPC - Enabling Infrastructure for HPC-Applications (DE/BMBF/13FH156IN6)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2022/02/01