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ill-defined nature of the space of possible primitives. The dual, complementary con-
ceptions provide two modes for describing and understanding change and creativity:
as the unfolding consequences of fixed combinatorial rules on bounded sets of pre-
defined primitives or as the effects of new covert processes and interactions that
come into play over time to provide new effective dimensional degrees of freedom.
We face several related problems. We want to know how to recognise creative
novelty when it occurs (the
methodological problem
). We also want to understand
the creative process in humans and other systems (the
scientific problem
) such that
creativity in human-machine collaborations can be enhanced and semi-autonomous,
creative devices can be built (the
design problem
).
The methodological problem can be solved by the “emergence-relative-to-a-
model” approach in which an observer forms a model of the behaviour of a system
(Sect.
15.4
). Novelty and creativity are inherently in the eye of the observer, i.e. rela-
tive to some model that specifies expected behaviours amongst possible alternatives.
If the behaviour changes, but it can still be predicted or tracked in terms of the basic
categories or state set of the model, one has rearrangement of trajectories of existing
states (combinatorial creativity). If behaviour changes, but in a manner that requires
new categories, observables, or states for the observer to regain predictability, then
one has the creation of new primitives (emergent creativity).
Solution of the scientific problem of creativity requires a clear description of what
creativity entails in terms of underlying generative and selective processes. Creativ-
ity exists in the natural world on many levels, from physical creation (particles,
elements, stars, galaxies) through the origins and evolution of life (multicellularity,
differentiated tissues, circulatory, nervous, and immune systems) to concept forma-
tion in brains and new modes of social organisation. What facilitating conditions
and organisations lead to such creativity? In biological evolutionary contexts the
main underlying mechanisms are Darwinian processes of genetic inheritance with
variation/recombination, genetically-steered phenotypic construction, and selection
by differential survival and reproduction. On the other hand, in neural contexts that
support creative learning processes the mechanisms appear to involve more directed,
Hebbian stabilisations of effective neural connectivities and signal productions.
Ultimately we seek to build artificial systems that can enhance human creativity
and autonomously create new ideas that we ourselves unaided by machines would
never have discovered. This will entail designing mechanisms for combinatorial
generation and for creation of new primitives. Essentially all adaptive, trainable ma-
chines harness the power of combinatorial spaces by finding ever better combina-
tions of parameters for classification, control, or pattern-generation. On the con-
temporary scene, a prime example is the genetic algorithm (Holland
1975
;
1998
),
which is a general evolutionary programming strategy (Fogel et al.
1966
) that per-
mits adaptive searching of high-dimensional, nonparametric combinatorial spaces.
Unfortunately, very few examples of artificial systems capable of emergent cre-
ativity are yet to be found. For the most part, this is due to the relative ease and
economy with which we humans, as opposed to machines, can create qualitatively
new solutions. We humans remain the pre-eminent generators of emergent creativity
