Procedural Video Game Scene Generation by Genetic and Neutrosophic WASPAS Algorithms

The demand for automated game development assistance tools can be fulfilled by computational creativity algorithms. The procedural generation is one of the topics for creative content development. The main procedural generation challenge for game level layout is how to create a diverse set of levels that could match a human-crafted game scene. Our game scene layouts are created randomly and then sculpted using a genetic algorithm. To address the issue of fitness calculation with conflicting criteria, we use weighted aggregated sum product assessment (WASPAS) in a single-valued neutrosophic set environment (SVNS) that models the indeterminacy with truth, intermediacy, and falsehood memberships. Results are presented as an encoded game object grid where each game object type has a specific function. The algorithm creates a diverse set of game scene layouts by combining game rules validation and aesthetic principles. It successfully creates functional aesthetic patterns without specifically defining the shapes of the combination of games’ objects.

Computational Creativity and Aesthetics with Algorithmic Information Theory

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.

A Preliminary Study for Literary Rhyme Generation based on Neuronal Representation, Semantics and Shallow Parsing

In recent years, researchers in the area of Computational Creativity have studied the human creative process proposing different approaches to reproduce it with a formal procedure. In this paper, we introduce a model for the generation of literary rhymes in Spanish, combining structures of language and neural network models The results obtained with a manual evaluation of the texts generated by our algorithm are encouraging.

On the Problem of Small Objects

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.

Generative Meditations: Exploring Generative Art From the Perspective of Tibetan Buddhist Philosophy

<p>the purpose of this thesis is to document and explore the subjective struggles I have encountered in my own practise as a generative artist rather than to provide an objective overview of computational generative art. Hopefully this process will give some context from the ground up (from an artist’s perspective) to some of the larger questions that I and others in the field are asking about generative art. From the preliminary questions arising from these struggles I begin to explore and develop a generative art practise that primarily focuses on the topics of human experience and ideas directly related to human experience. This is opposed to using generative processes to explore ideas fundamentally based on computation (a-life, emergence, computational creativity, and data etc..). The foundation of, and reasons behind, such a focus are based on the non-realist and non-materialist philosophical tenets of Tibetan Buddhism, in particular the philosophy of the Madhyamika-Prasangika school of thought. The purpose of developing a generative practise based on the philosophy and symbolism of Tibetan Buddhism is to find a method to create personally relevant artwork with a firm foundation in a well established culture of art and philosophy. I might add however, that this isn’t merely a self-reflective exercise but rather it should be of interest to others in the field of (and study of) Generative art to see how this artistic method might be approached from a vastly different philosophical stance to the materialist view that receives the majority of attention in the field.</p>

Generative Meditations: Exploring Generative Art From the Perspective of Tibetan Buddhist Philosophy

<p>the purpose of this thesis is to document and explore the subjective struggles I have encountered in my own practise as a generative artist rather than to provide an objective overview of computational generative art. Hopefully this process will give some context from the ground up (from an artist’s perspective) to some of the larger questions that I and others in the field are asking about generative art. From the preliminary questions arising from these struggles I begin to explore and develop a generative art practise that primarily focuses on the topics of human experience and ideas directly related to human experience. This is opposed to using generative processes to explore ideas fundamentally based on computation (a-life, emergence, computational creativity, and data etc..). The foundation of, and reasons behind, such a focus are based on the non-realist and non-materialist philosophical tenets of Tibetan Buddhism, in particular the philosophy of the Madhyamika-Prasangika school of thought. The purpose of developing a generative practise based on the philosophy and symbolism of Tibetan Buddhism is to find a method to create personally relevant artwork with a firm foundation in a well established culture of art and philosophy. I might add however, that this isn’t merely a self-reflective exercise but rather it should be of interest to others in the field of (and study of) Generative art to see how this artistic method might be approached from a vastly different philosophical stance to the materialist view that receives the majority of attention in the field.</p>

Criticisms of digital art: foundations and theoretical limits

This article attempts a critical examination of the actual digital art production, posing key questions about its artistic value inside the contemporary esthetic discourse, including the relationships with the art system. Considering the properties of the numeric language, two important topics will be discussed: the dialectic artwork-process and the meaning of digital multimedia. With the support of up-to-date computational creativity theories and the evidences of experimental digital art, the treacherous influence of the romantic aesthetic paradigm and the traditional art system will be tested. As a possible solution, the author suggests rethinking the components of software as meta-medium, such as its information assets, its algorithms, the code textual properties, interactivity and interfaces. In the conclusions it will be stressed that the immateriality of software is paramount to avoid misunderstandings and to open new fields of research. For instance, natural computation, ethnomathematics and ethnocomputing are valuable social and educational contents and meanings that could substantially improve digital art.

A computational creativity approach from music to images

Our system generates abstract images from music that serve as inspiration for the creative process. We developed one of many possible approaches for a cross-domain association between the musical and visual domains, by extracting features from MIDI music files and associating them to visual characteristics. The associations were led by the authors' aesthetic preferences and some experimentation. Three different approaches were pursued, two with direct or random associations and a third using a genetic algorithm that considers music and color theory while searching for better results. The resulting images were evaluated through online surveys, which confirmed that not only they were abstract, but also that there was a relationship with the music that served as the basis for the association process. Moreover, the majority of the participants ranked highest the images improved with the genetic algorithm. This newsletter contribution summarizes the full version of the article, which was presented at EvoMUSART 2021 (the 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design).