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Changes in interest result in goals being updated, and the current ever-changing
goals determine movement.
Unsupervised artificial neural networks are used for classification, and classifi-
cation error for new inputs is interpreted as novelty. But greater novelty doesn't
necessarily result in greater interest. The psychologist Daniel Berlyne proposed that
piquing interest requires a balance of similarity to previous experience and novelty.
So, as suggested by Berlyne ( 1960 ; 1971 ), a Wundt curve is used to provide the
metric for this balance and produces an appropriate interest measure. More about
Berlyne's work follows in Sect. 10.3.2 .
Based on this model Sanders created an experimental simulation where agents
enter a gallery, can sense other agents, and can also view the colours of monochrome
paintings hanging on nearby walls. There are also unseen monochrome paintings
with new colours in other rooms. Along with other social behaviours agents learn the
colours presented in one room, and then are potentially curious about new colours in
other rooms. Depending on the sequence of colour exposure and the related Wundt-
curve mapping, agents may or may not develop an interest and move to other areas.
10.2.11.5 Human Aesthetics, Meta-aesthetics, and Alternatives to Fitness
Functions
Commenting on systems like those above using coevolution, niche creation, swarms,
and curiosity Dorin ( 2005 ) notes:
. . . the “ecosystemic” approach permits simultaneous, multidirectional and automatic ex-
ploration of a space of virtual agent traits without any need for a pre-specified fitness func-
tion. Instead, the fitness function is implicit in the design of the agents, their virtual envi-
ronment, and its physics and chemistry.
This avoids the problem of creating a computational aesthetic evaluation system
by hand, and allows for the creation of evolutionary systems that generate surprising
diversity and increased dynamics. Thus, if the goal is the creation of robust systems
for meta-aesthetic exploration these evolutionary system extensions seem to be quite
beneficial.
However, if the goal is to evolve results that appeal to our human sense of aes-
thetics there is no reason to think that will happen. Recall the earlier differentiation
between human aesthetic evaluation and meta-aesthetic explorations. Creating evo-
lutionary diversity and dynamics via artificial aesthetics foreign to our human sen-
sibility is one thing. Appealing to human aesthetics is quite another. As observed by
Todd and others, to date extensions and emergent aesthetics like those above do not
provide machine evaluation that mirrors human aesthetic perception.
10.2.12 Complexity Based Models of Aesthetics
One of the recurring themes in computational aesthetics is the notion that aes-
thetic value has something to do with a balance of complexity and order. Birkhoff's
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