Weak and Strong Computational Creativity - Computational Creativity Research: Towards Creative Machines - page 43

behaviour (i.e. there is only small random distance and direction from the points of
the original line drawing); comparing the two sketches, we note a lack of any sig-
nificant difference between them. Furthermore, when more 'freedom' is granted to
the randomised algorithm (by increasing the range in the underlying random number
generator, which allows the technique to explore broader areas of the canvas), the
algorithm soon begins to deviate excessively from the original line drawing. For this
reason such randomisation results in a very poor—low fidelity—interpretation of
the original line drawing (Fig. 2.5 -bottom). In contrast, although the agents in the
swarms are free to access any part of the canvas, the swarm-control mechanism (i.e.
SwarmFreedom) naturally enables the system tomaintain recognisable fidelity to the
original input. In the randomised algorithm, contra the swarms system, it can be seen
that simply by giving the agents more randomised behaviour (Random Freedom),
they fail to produce more 'creative sketches'.
The Swarmic Freedom or 'controlled freedom' (or the 'tincture of madness')
exhibited by the swarm algorithms (induced by the stochastic side of the algorithms)
is crucial to the resultant work and is the reason why having the same line drawing
does not result in the system producing identical sketches. This freedom emerges,
among other influencing factors, from the stochasticity of the SDS algorithm in
picking agents for communication, as well as choosing agents to diffuse information;
the tincture of madness in the PSO algorithm is induced via its strategy of spreading
the particles throughout the search space as well as the stochastic elements in deciding
the next move of each particle.
In other words, the reason why the swarm sketches are different from the sim-
ple randomised sketches, is that the underlying PSO flocking component-algorithm
constantly endeavours to accurately trace the input image whilst the SDS foraging
component constantly endeavours to explore the wider canvas (i.e. together the two
swarm mechanisms ensure high-level fidelity to the input without making an exact
low-level copy of the original line drawing). Although the algorithms (PSO and SDS)
are nature-inspired, we do not claim that the presented work is an accurate model of
natural systems. Furthermore, whilst designing the algorithm there was no explicit
'Hundertwasser-like' attempt [ 30 ] by which we mean the stress on using curves
instead of straight lines, as Hundertwasser considered straight lines not nature-like
and tried not to use straight lines in his works to bias the style of the system's sketches.
2.4 Weak Versus Strong Computational Creativity
Before approaching the topic of weak or strong computational creativity, the
difference between weak and strong AI is highlighted. In strong AI, the claim is
that machines can think and have genuine understanding and other cognitive states
(e.g. “suitably programmed machines will be capable of conscious thought” [ 12 ]);
weak AI, in contrast, does not usually go beyond expecting the simulation of human
intelligence. I.e. instantiating genuine “understanding” is not the primary concern in
weak AI research.