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Soma (self-organizing map ants)

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Soma 416 frame 159, screen image from volatile sequence, 4200 × 600 pixels, 2005. (© Tim Barrass)

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Soma 435 frame 026, screen image from volatile sequence, 4200 × 600 pixels, 2005. (© Tim Barrass)

SOMA is a software model of a dynamic system in which a virtual population of ant-like drawing agents develops individual behaviors in response to marks on a surface that they collectively modify. A small neural network called a Kohonen self-organizing map (SOM), which responds to nearby patterns on the drawing surface, regulates the motion of each ant. The SOM determines the ant’s next move and is modified by the most recent pattern in the process. Each ant leaves a trail, contributing to the overall image. When thousands of ants interact this way, a complex multi-directional feedback system in which agents indirectly influence one another’s internal structures forms through the effects the ants have on their surroundings. It is difficult to predict what will happen without running the system.

The images reproduced here were taken from separate runs, with different starting configurations, numbers of ants, trail persistence, sensor configurations, movement capabilities, individual trail colors and color responses. I had a bias in seeking to find combinations of parameters that would lead to clear yet constantly changing visual structures, while avoiding sequences that either dissolved to gray or seemed to get stuck in a rut. The runs are intended to be viewed “live” during the process of development, without a set time limit.

Tim Barrass
Email: <barrasstim@gmail.com>

You Pretty Little Flocker

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Small Blue Triple, 3000 × 900 pixels, still from digital animation, 2009. (© Alice Eldridge)

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Pobpom with variable size preferences (max, mid, min), 1000 × 1000 pixels, still from digital animation, 2009. (© Alice Eldridge)

Even when we know exactly what is going on in formal terms, the dynamics of many models of collective behavior are compelling: The emergent behavior is often uncannily lifelike and belies its origin in simple self-organizing mechanisms. A basic bio-logic shines through, even in static representations. Such models constitute a rich compendium for the generative arts. But how do we go beyond simply visualizing scientific simulations and manipulate these models for use in design and creative art contexts?

When we play with these models, it becomes apparent that the emergent dynamics occupy only a fraction of the entire potential state-space of the system. You Pretty Little Flocker began as a technical study exploring issues of control, manipulation and representation in algorithmic arts: How might we expand the space of possibilities? How might we steer the system through these models without sabotaging the core self-organizing processes? Is the generative potential or aesthetic appeal of these systems inherent in the model or tied to particular rendering methods?

Alice Eldridge
Email: <alice@ecila.org>

Ant Paintings

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Ant Painting #21529, 6 × 6 inches, digital print, 2004. (© Gary Greenfield)

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Ant Painting #22360, 6 × 6 inches, digital print, 2006. (© Gary Greenfield)

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Ant Painting #6697, 10.5 × 10.5 inches, digital print, 2012. (© Gary Greenfield)

I first became aware of ant paintings when Nicolas Monmarché spoke about them at the 2003 Congress on Evolutionary Computation (CEC) Conference. My first project used non-interactive artificial evolution to evolve ant paintings where two castes of ants explored and exploited their environment based on color cues.

One of the most fascinating things about ants is their use of pheromones. My second project also used non-interactive evolution to evolve ant paintings, but this time two colonies competed for territory by chemically sensing food in the environment while also using pheromones they deposited to help control following and avoidance behaviors.

The variety of behaviors exhibited by different species of ants is astounding. Stanford biologist Deborah Gordon studies the seed-harvesting ant species P. barbatus. Foragers of this species stream out along well-established trails before scattering to find seeds, which they return to the nest using the shortest route possible. My most recent project stylizes this behavior by having virtual ants of this species stream out along...

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