Special Issue "Artificial Intelligence and Complexity in Art, Music, Games and Design II"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 5393

Special Issue Editors

Dr. Colin Johnson
E-Mail Website
Guest Editor
School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
Interests: artificial intelligence; bio-inspired computation; evolutionary computation; neural networks; computational arts; computer music
Special Issues, Collections and Topics in MDPI journals
Dr. Juan Romero
E-Mail Website
Guest Editor
Computation Department, Universidade da Coruña, Coruña, A, Spain
Interests: artificial intelligence; artificial art; computational aesthetics; evolutionary computation; artificial neural networks; deep learning; machine learning; computational creativity
Special Issues, Collections and Topics in MDPI journals
Dr. Tiago Martins
E-Mail Website
Guest Editor
CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
Interests: artificial intelligence; computer art; design; generative design; evolutionary computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A major—potentially unachievable—challenge in computational arts is constructing algorithms that assess properties such as novelty, creativity and the aesthetic properties of artistic artefacts or performances. Approaches to this have often been based on broadly information-theoretic ideas. For example, ideas linking mathematical notions of form and balance to beauty date back to ancient times. In the twentieth century, attempts were made to produce aesthetic measures based on ideas of a balance between order and complexity. In more recent years, these have been formalised into ideas of aesthetic engagement happening when work is at the “edge of chaos” between excessive order and excessive disorder, formalising this using notions such as Gini coefficient and Shannon entropy, and links between cognitive theories of the Bayesian brain and free energy minimisation with aesthetic theories. These ideas have been used both for understanding human behaviour and building creative systems.

The use of artificial intelligence and complex systems for the development of artistic systems is an exciting and relevant area of research. There is a growing interest in the application of these techniques in fields, such as: visual art and music generation, analysis, and interpretation, sound synthesis, architecture, video, poetry, design, game content generation, and other creative tasks.

This Special Issue will focus on both the use of complexity ideas and artificial intelligence methods to analyse and evaluate aesthetic properties and to drive systems that generate aesthetically engaging artefacts, including but not limited to: music, sound, images, animations, designs, architectural plans, choreographies, poetry, text, jokes, etc.

Volume I: https://www.mdpi.com/journal/entropy/special_issues/ai_complexity

Dr. Colin Johnson
Dr. Juan Romero
Dr. Tiago Martins
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

• Computational aesthetics
• Formalising ideas of aesthetics using ideas from entropy and information theory
• Computational Creativity
• Artificial Intelligence in art, design, architecture, music and games
• Information Theory in art, design, architecture, music and games
• Complex systems in art, music and design
• Evolutionary art
• Evolutionary music
• Artificial life in arts
• Swarm art
• Pattern recognition and aesthetics
• Cellular automata in architecture
• Computational intelligence in art

Published Papers (7 papers)

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Research

Article
Composing Music Inspired by Sculpture: A Cross-Domain Mapping and Genetic Algorithm Approach
Entropy 2022, 24(4), 468; https://doi.org/10.3390/e24040468 - 28 Mar 2022
Viewed by 360
Abstract
In this article, a system that takes a 3D model of a sculpture as starting point to compose music is presented. We raised the hypothesis that cross-domain mapping can be an approach to model inspiration. The semantic meaning of the sculpture is not [...] Read more.
In this article, a system that takes a 3D model of a sculpture as starting point to compose music is presented. We raised the hypothesis that cross-domain mapping can be an approach to model inspiration. The semantic meaning of the sculpture is not used directly but rather a more abstract approach was used. A Genetic Algorithm was used to obtain results with more musical interest. The results were promising: the majority of the participants gave a classification of 4 out of 5 to the preferred interpretations of the compositions and related them to the respective sculpture. This is a step toward a possible model for inspiration. Full article
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Article
Validation of an Aesthetic Assessment System for Commercial Tasks
Entropy 2022, 24(1), 103; https://doi.org/10.3390/e24010103 - 09 Jan 2022
Cited by 1 | Viewed by 350
Abstract
Automatic prediction of the aesthetic value of images has received increasing attention in recent years. This is due, on the one hand, to the potential impact that predicting the aesthetic value has on practical applications. Even so, it remains a difficult task given [...] Read more.
Automatic prediction of the aesthetic value of images has received increasing attention in recent years. This is due, on the one hand, to the potential impact that predicting the aesthetic value has on practical applications. Even so, it remains a difficult task given the subjectivity and complexity of the problem. An image aesthetics assessment system was developed in recent years by our research group. In this work, its potential to be applied in commercial tasks is tested. With this objective, a set of three portals and three real estate agencies in Spain were taken as case studies. Images of their websites were taken to build the experimental dataset and a validation method was developed to test their original order with another proposed one according to their aesthetic value. So, in this new order, the images that have the high aesthetic score by the AI system will occupy the first positions of the portal. Relevant results were obtained, with an average increase of 52.54% in the number of clicks on the ads, in the experiment with Real Estate portals. A statistical analysis prove that there is a significant difference in the number of clicks after selecting the images with the AI system. Full article
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Article
Network Bending: Expressive Manipulation of Generative Models in Multiple Domains
Entropy 2022, 24(1), 28; https://doi.org/10.3390/e24010028 - 24 Dec 2021
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Abstract
This paper presents the network bending framework, a new approach for manipulating and interacting with deep generative models. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network [...] Read more.
This paper presents the network bending framework, a new approach for manipulating and interacting with deep generative models. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference. In addition, we present a novel algorithm for analysing the deep generative model and clustering features based on their spatial activation maps. This allows features to be grouped together based on spatial similarity in an unsupervised fashion. This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated results. We outline this framework, demonstrating our results on deep generative models for both image and audio domains. We show how it allows for the direct manipulation of semantically meaningful aspects of the generative process as well as allowing for a broad range of expressive outcomes. Full article
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Article
Computational Creativity and Aesthetics with Algorithmic Information Theory
Entropy 2021, 23(12), 1654; https://doi.org/10.3390/e23121654 - 08 Dec 2021
Cited by 1 | Viewed by 766
Abstract
We build an analysis based on the Algorithmic Information Theory of computational creativity and extend it to revisit computational aesthetics, thereby, improving on the existing efforts of its formulation. We discuss Kolmogorov complexity, models and randomness deficiency (which is a measure of [...] Read more.
We build an analysis based on the Algorithmic Information Theory of computational creativity and extend it to revisit computational aesthetics, thereby, improving on the existing efforts of its formulation. We discuss Kolmogorov complexity, models and randomness deficiency (which is a measure of how much a model falls short of capturing the regularities in an artifact) and show that the notions of typicality and novelty of a creative artifact follow naturally from such definitions. Other exciting formalizations of aesthetic measures include logical depth and sophistication with which we can define, respectively, the value and creator’s artistry present in a creative work. We then look at some related research that combines information theory and creativity and analyze them with the algorithmic tools that we develop throughout the paper. Finally, we assemble the ideas and their algorithmic counterparts to complete an algorithmic information theoretic recipe for computational creativity and aesthetics. Full article
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Article
On the Problem of Small Objects
Entropy 2021, 23(11), 1524; https://doi.org/10.3390/e23111524 - 16 Nov 2021
Cited by 1 | Viewed by 453
Abstract
We discuss how to assess computationally the aesthetic value of “small” objects, namely those that have short digital descriptions. Such small objects still matter: they include headlines, poems, song lyrics, short musical scripts and other culturally crucial items. Yet, small objects are a [...] Read more.
We discuss how to assess computationally the aesthetic value of “small” objects, namely those that have short digital descriptions. Such small objects still matter: they include headlines, poems, song lyrics, short musical scripts and other culturally crucial items. Yet, small objects are a confounding case for our recent work adapting ideas from algorithmic information theory (AIT) to the domain of computational creativity, as they cannot be either logically deep or sophisticated following the traditional definitions of AIT. We show how restricting the class of models under analysis can make it the case that we can still separate high-quality small objects from ordinary ones, and discuss the strengths and limitations of our adaptation. Full article
Article
Statistical and Visual Analysis of Audio, Text, and Image Features for Multi-Modal Music Genre Recognition
Entropy 2021, 23(11), 1502; https://doi.org/10.3390/e23111502 - 12 Nov 2021
Viewed by 520
Abstract
We present a multi-modal genre recognition framework that considers the modalities audio, text, and image by features extracted from audio signals, album cover images, and lyrics of music tracks. In contrast to pure learning of features by a neural network as done in [...] Read more.
We present a multi-modal genre recognition framework that considers the modalities audio, text, and image by features extracted from audio signals, album cover images, and lyrics of music tracks. In contrast to pure learning of features by a neural network as done in the related work, handcrafted features designed for a respective modality are also integrated, allowing for higher interpretability of created models and further theoretical analysis of the impact of individual features on genre prediction. Genre recognition is performed by binary classification of a music track with respect to each genre based on combinations of elementary features. For feature combination a two-level technique is used, which combines aggregation into fixed-length feature vectors with confidence-based fusion of classification results. Extensive experiments have been conducted for three classifier models (Naïve Bayes, Support Vector Machine, and Random Forest) and numerous feature combinations. The results are presented visually, with data reduction for improved perceptibility achieved by multi-objective analysis and restriction to non-dominated data. Feature- and classifier-related hypotheses are formulated based on the data, and their statistical significance is formally analyzed. The statistical analysis shows that the combination of two modalities almost always leads to a significant increase of performance and the combination of three modalities in several cases. Full article
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Article
Evolution of Entropy in Art Painting Based on the Wavelet Transform
Entropy 2021, 23(7), 883; https://doi.org/10.3390/e23070883 - 11 Jul 2021
Viewed by 817
Abstract
Quantitative studies of art and aesthetics are representative of interdisciplinary research. In this work, we conducted a large-scale quantitative study of 36,000 paintings covering both Eastern and Western paintings. The information entropy and wavelet entropy of the images were calculated based on their [...] Read more.
Quantitative studies of art and aesthetics are representative of interdisciplinary research. In this work, we conducted a large-scale quantitative study of 36,000 paintings covering both Eastern and Western paintings. The information entropy and wavelet entropy of the images were calculated based on their complexity and energy. Wavelet energy entropy is a feature that can characterize rich information in images, and this is the first study to introduce this feature into aesthetic analysis of art paintings. This study shows that the process of entropy change coincides with the development process of art painting. Further, the experimental results demonstrate an important change in the evolution of art painting, and since the rise of modern art in the twentieth century, the entropy values in painting have started to become diverse. In comparison with Western paintings, Eastern paintings have distinct low entropy characteristics in which the wavelet entropy feature of the images has better results in the machine learning classification task of Eastern and Western paintings (i.e., the F1 score can reach 97%). Our study can be the basis for future quantitative analysis and comparative research in the context of Western and Eastern art aesthetics. Full article
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