Towards Machine Learning as an Enabler of Computational Creativity

Mateja, Deborah; Heinzl, Armin

doi:10.1109/tai.2021.3100456

Cited by 20 publications

(9 citation statements)

References 57 publications

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“…Searching through a space of possibilities is an essential aspect of the creative process (Boden, 2004; Hills et al, 2015; Mateja & Heinzl, 2021; Todd et al, 2012; Tromp, 2023; Veale et al, 2019). The idea of searching within a problem-space, and retaining the best candidate solution, is also central to computational implementations of creativity (Gardner, 1985; Turing, 1936).…”

Section: Defining the Spaces Of Possibilitiesmentioning

confidence: 99%

The Creative Space Theory as a map to explore the mind

Goulet-Pelletier,

Cousineau

2024

Possibility Studies & Society

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Despite significant transformations in most domains of activities, there might still be some constancies in the creative spaces explored throughout history. This paper introduces the Creative Space Theory (CST), a conceptual framework delineating 10 distinct creative spaces, analogous to creative landscapes. These creative spaces are proposed as navigational terrains for an array of media, tools, activities, and domains. The 10 spaces of the theory are movement, sound, image, sensation, emotion, strategy, story, symbol, network, and system. Notably, these creative spaces transcend specific media, and cover artistic as well as intellectual domains. For example, the sound space would be relevant to music, poetry, filmmaking, and acting among others, whereas the system space may be relevant to engineering, medicine, science, and design among others. The proposed theory holds potential utility in three key areas: (1) nurturing individual’s creative potential, (2) helping creators adapt to continuously changing circumstances, and (3) fostering positive creative self-beliefs in overlooked domains of creation. The current paper is a theoretical elaboration. We describe the creative spaces and discuss the implications of the theory towards individuals, educational practices, and research within the fields of cognition and Artificial Intelligence.

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Section: Defining the Spaces Of Possibilitiesmentioning

confidence: 99%

The Creative Space Theory as a map to explore the mind

Goulet-Pelletier,

Cousineau

2024

Possibility Studies & Society

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“…While there is much research dealing with explicating the rules of aesthetics and finding ways to statically evaluate it (Ngo et al 2002;Tractinsky et al 2000;2006), such approaches are context-specific and cannot be applied to just any object in an observer-independent way. As a remedy, ML methods were experimented and found successful in capturing implicit and subjective phenomena like aesthetics from human-rated examples of a target class (Bansal and Bhattacharya 2013, Dhengre et al 2020, Mateja and Heinzl 2021, Murray et al 2012, Ramesh et al 2022, Wang et al 2016.…”

Section: Related Workmentioning

confidence: 99%

AnimateSVG: Autonomous Creation and Aesthetics Evaluation of Scalable Vector Graphics Animations for the Case of Brand Logos

Mateja,

Armbruster,

Baumert

et al. 2023

AAAI

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In the light of the constant battle for attention on digital media, animating digital content plays an increasing role in modern graphic design. In this study, we use artificial intelligence methods to create aesthetic animations along the case of brand logos. With scalable vector graphics as the standard format in modern graphic design, we develop an autonomous end-to-end method using complex machine learning techniques to create brand logo animations as scalable vector graphics from scratch. We acquire data and setup a comprehensive animation space to create novel animations and evaluate them based on their aesthetics. We propose and compare two alternative computational models for automated logo animation and carefully weigh up their idiosyncrasies: on the one hand, we set up an aesthetics evaluation model to train an animation generator and, on the other hand, we combine tree ensembles with global optimization. Indeed, our proposed methods are capable of creating aesthetic logo animations, receiving an average rating of ‘good’ from observers.

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“…As identified by the authors of [14], there are four components that categorize creativity: motivation, available knowledge, past experiences, and preference. Agreeing with [14], Mateja and Heinzl in [17] discuss the 4 P' model of creativity, which includes aspects such as Person (Producer), Process, Product, and Press/Environment. The 4 P' model of creativity was first proposed by Anna Jordanous in [18], where she defined Person as the creative individual agent, Process as the actions taken by the identified Person, Product as the result produced by the Process, and Press as the situation where the creative process takes place.…”

Section: Definition Of Creativitymentioning

confidence: 99%

“…The 4 P' model of creativity was first proposed by Anna Jordanous in [18], where she defined Person as the creative individual agent, Process as the actions taken by the identified Person, Product as the result produced by the Process, and Press as the situation where the creative process takes place. The authors in [17] further break down these four aspects into task-specific components by explaining how society can greatly influence task motivations and how domain-relevant skills can contribute to building machine-based creativity. Understanding the composition of creativity is essential to this research project as it allows this research to quantify the concept of creativity through method designs.…”

Section: Definition Of Creativitymentioning

confidence: 99%

Reforming Intellectual Property Right Based On Public Preferences Towards Artificial Intelligence Generated Music

Ho¹,

Huang²,

Chang³

2022

Preprint

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<p>This study examines how legal frameworks should set a clear definition for Artificial Intelligence’s (AI) role in creative work through the perceptions of the younger generation on AI-generated music. Using a survey composed of 6 components (Perceived Quality, Perceived Imaginativeness, Spatial Presence, Empathy, and Musician’s Competence), this study sampled the technology and art related intended majors sampling groups to determine whether the computational music passes the Turing test. After listening to two samples with hidden artist identities (Human vs AI), the participants (n = 35) were asked to evaluate the samples presented. Then, artist identities are revealed and participants were asked to make changes to their initial responses. This study found that participants have similar appraisals towards human-composed music and AI generated music while there is a significant difference between the initial attitudes towards AI for the two sampling groups. For participants who changed their responses, there are no significant changes in attitude but an overall negative shift in responses is observed. This study concluded that the possibility for the younger generation to recognize AI as actual artists and thus supporting AI IP rights is low. Further implications of the findings and future directions for research are discussed. </p>

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Towards Machine Learning as an Enabler of Computational Creativity

Cited by 20 publications

References 57 publications

The Creative Space Theory as a map to explore the mind

The Creative Space Theory as a map to explore the mind

AnimateSVG: Autonomous Creation and Aesthetics Evaluation of Scalable Vector Graphics Animations for the Case of Brand Logos

Reforming Intellectual Property Right Based On Public Preferences Towards Artificial Intelligence Generated Music

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