The Digital Synaptic Neural Substrate: A New Approach to Computational Creativity
Azlan Iqbal, Matej Guid, Simon Colton, Jana Krivec, Shazril Azman,, Boshra Haghighi

TL;DR
The paper introduces the Digital Synaptic Neural Substrate (DSNS), a novel AI method that recombines attributes from diverse domains to generate creative objects, demonstrated here by producing high-quality chess problems.
Contribution
It presents the DSNS approach, enabling cross-domain attribute recombination for automated creative content generation, validated through chess problem composition.
Findings
Generated chess problems of high aesthetic quality
Low-quality sources combined with photographs outperform high-quality sources
Approach is scalable to any domain with attribute-representable objects
Abstract
We introduce a new artificial intelligence (AI) approach called, the 'Digital Synaptic Neural Substrate' (DSNS). It uses selected attributes from objects in various domains (e.g. chess problems, classical music, renowned artworks) and recombines them in such a way as to generate new attributes that can then, in principle, be used to create novel objects of creative value to humans relating to any one of the source domains. This allows some of the burden of creative content generation to be passed from humans to machines. The approach was tested in the domain of chess problem composition. We used it to automatically compose numerous sets of chess problems based on attributes extracted and recombined from chess problems and tournament games by humans, renowned paintings, computer-evolved abstract art, photographs of people, and classical music tracks. The quality of these generated chess…
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Taxonomy
TopicsCreativity in Education and Neuroscience · Advanced Memory and Neural Computing · Neural Networks and Applications
