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food and brings it back to the nest it selectively leaves a chemical pheromone trail.
Other ants happening upon the chemical trail will follow it, in effect joining a food
retrieval swarm. Each ant adds more pheromone as they retrieve food. Because the
pheromone spreads as it dissipates ants will discover short cuts if the initial path
has excessive winding. In turn those short cuts will become reinforced with addi-
tional pheromone. Once the food is gone the ants stop laying down pheromone as
they leave the now depleted site, and soon the pheromone trail will disappear. This
behaviour can be simulated in software agents (Resnick
1994
).
Artists have simulated this behaviour in software using agents that lay down
permanent virtual pigment as well as temporary virtual pheromone trails. Variation
and some degree of aesthetic control can be gained by breeding the ant-agents using
an interactive evolutionary system (Monmarché et al.
2003
).
Greenfield (
2005a
) automates the fitness function based on a performance metric
regarding the number of cells visited randomly or due to pheromone following be-
haviour. Measuring fitness based only on the number of unique cells visited results
in “monochromatic degeneracies”. Rewarding only pheromone following creates a
slightly more attractive blotchy style. Various weightings of both behaviours pro-
duce the best aesthetic results exhibiting organic and layered forms.
Urbano (
2006
) has produced striking colourful patterns using virtual micro-
painters he calls “Gaugants”. In the course of one-to-one transactions his agents
exert force, form consensus, or exhibit dissidence regarding paint colour. The dy-
namics are somewhat reminiscent of scenarios studied in game theory. Elzenga's
agents are called “Arties”. They exhibit mutual attraction/repulsion behaviour based
on multiple sensing channels and genetic predisposition. The exhibited emergence
is difficult to anticipate, but the artist can influence the outcome by making manual
selections from within the gene pool (Elzenga and Pontecorvo
1999
).
10.2.11.4 Curious Agents
Saunders and Gero (
2004
), and Saunders (
2002
) have extended swarming agents to
create what they have called
curious agents
. They first note that agents in swarm
simulations such as the above are mostly reactive. Flocking was originally devel-
oped by Reynolds (
1987
) and then extended by Helbing and Molnar (
1995
;
1997
)to
add
social forces
such as goals, drives to maximise efficiency and minimise discom-
fort, and so on. Social forces have been shown, for example, to create advantages in
foot traffic simulation.
Sanders and Gero expand the dynamics of aesthetic evaluation behaviour by
adding curiosity as a new social force. Their implementation uses a pipeline of six
primary modules for sensing, learning, detecting novelty, calculating interest, plan-
ning, and acting. Sensing provides a way to sample the world for stimulus patterns.
Learning involves classifying a pattern and updating prototypes kept in long term
memory. Novelty is assessed as the degree to which error or divergence from pre-
vious prototypes is detected. Based on novelty a measure of interest is calculated.
