A Developmental Model for Generative Media

McCormack, Jon

doi:10.1007/11553090_10

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…Formal grammars have been suitable to express this inner structured nature of music, for example to study melody [21], musical structure [22] or harmony in tonal music, even with a generative approach [23]. L-systems [24] in particular have been frequently used as representational models in evo-devo processes due to their simplicity, sometimes with an interactive fitness and studying ways of visualizing music [25,26].…”

Section: Automated Compositionmentioning

confidence: 99%

Using Formal Grammars as Musical Genome

et al. 2021

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In this paper, we explore a generative music method that can compose atonal and tonal music in different styles. One of the main differences between regular engineering problems and artistic expressions is that goals and constraints are usually ill-defined in the latter case; in fact the rules here could or should be transgressed more regularly. For this reason, our approach does not use a pre-existing dataset to imitate or extract rules from. Instead, it uses formal grammars as a representation method than can retain just the basic features, common to any form of music (e.g., the appearance of rhythmic patterns, the evolution of tone or dynamics during the composition, etc.). Exploring different musical spaces is the responsibility of a program interface that translates musical specifications into the fitness function of a genetic algorithm. This function guides the evolution of those basic features enabling the emergence of novel content. In this study, we then assess the outcome of a particular music specification (guitar ballad) in a controlled real-world setup. As a result, the generated music can be considered similar to human-composed music from a perceptual perspective. This endorses our approach to tackle arts algorithmically, as it is able to produce novel content that complies with human expectations.

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Section: Automated Compositionmentioning

confidence: 99%

Using Formal Grammars as Musical Genome

et al. 2021

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“…These works typically use specific grammars designed for the corresponding problems. A more generalized study on grammatical generation was carried out with the cellular programming model (McCormack 2005). Our work is inspired by the need from these and other works for a convenient and generalized tool for encoding grammatical PCGs, and therefore can potentially ease their implementation.…”

Section: Grammatical Pcgmentioning

confidence: 99%

GIGL: A Domain Specific Language for Procedural Content Generation with Grammatical Representations

Chen

Guy

2018

AIIDE

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We introduce a domain specific language for procedural content generation (PCG) called Grammatical Item Generation Language (GIGL). GIGL supports a compact representation of PCG with stochastic grammars where generated objects maintain grammatical structures. Advanced features in GIGL allow flexible customizations of the stochastic generation process. GIGL is designed and implemented to have direct interface with C++, in order to be capable of integration into production games. We showcase the expressiveness and flexibility of GIGL on several representative problem domains in grammatical PCG, and show that the GIGL-based implementations run as fast as comparable C++ implementation and with less code.

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“…The software uses a rule-based developmental model, loosely based on biological morphogenesis and derived from earlier work in developmental grammars such as Lsystems (Prusinkiewicz & Lindenmayer 1990). Technical details can be found in (McCormack 2005). In simple terms, the software simulates the growth and development of the form from a series of developmental rules, metaphorically like the way DNA "encodes" the developmental plans of biological organisms.…”

Section: Fifty Sistersmentioning

confidence: 99%

Working with Generative Systems: An Artistic Perspective

McCormack

2017

Electronic Workshops in Computing

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Generative systems are unique in that they allow the artist to specify processes rather than outcomes. In this paper, I describe my creative approach to working with complex computational systems that I have designed and coded over several decades. These systems require certain ways of working that offer both liberation and restriction on creative practice. To illustrate how these systems work in a creative sense, I will give an overview of two recent works: Fifty Sisters (2012-2016) and The Unknowable (2015-2017). Fifty Sisters comprises of fifty 1m × 1m images of computer synthesised plant-forms, algorithmically "grown" from computer code using artificial evolution and generative grammars. Each plant-like form is derived from the primitive graphic elements of oil company logos. The title of the work refers to the original "Seven Sisters" -a cartel of seven oil companies that dominated the global petrochemical industry and Middle East oil production from the mid-1940s until the oil crisis of the 1970s. The Unknowable is a three-channel 4k video triptych. The work depicts a series of virtual flora, evolved using computer programs based on biological evolution. In each sequence evolved plant species slowly disintegrate and then reform, breaking into their fundamental component -the polygon -then reforming anew in an endless cycle of destruction and reconstitution. Both works are mediations on our changed relationship to nature and the natural in a globalised, disconnected virtual world now effectively navigated through digital media.

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A Developmental Model for Generative Media

Cited by 7 publications

References 10 publications

Using Formal Grammars as Musical Genome

Using Formal Grammars as Musical Genome

GIGL: A Domain Specific Language for Procedural Content Generation with Grammatical Representations

Working with Generative Systems: An Artistic Perspective

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