  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 problems was then assessed
automatically using an existing and experimentally-validated computational
chess aesthetics model. They were also assessed by human experts in the domain.
The results suggest that attributes collected and recombined from chess and
other domains using the DSNS approach can indeed be used to automatically
generate chess problems of reasonably high aesthetic quality. In particular, a
low quality chess source (i.e. tournament game sequences between weak players)
used in combination with actual photographs of people was able to produce
three-move chess problems of comparable quality or better to those generated
using a high quality chess source (i.e. published compositions by human
experts), and more efficiently as well. Why information from a foreign domain
can be integrated and functional in this way remains an open question for now.
The DSNS approach is, in principle, scalable and applicable to any domain in
which objects have attributes that can be represented using real numbers.
