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Computational Creativity in Visual Arts

visual arts

This article addresses the question of the possibility of achieving computational creativity through some examples of computer programs capable of replicating some aspects of creative behavior in the fields of music and visual arts. The reason for focusing on these artistic fields is that they are by far the ones in which there is more activity and where the results obtained are most impressive.

Computational Creativity in Visual Arts

AARON is a robotic system, developed over many years by the artist and programmer Harold Cohen (Cohen, 1995), that can pick up a paintbrush with its robotic arm and paint on canvas on its own. It draws people in a botanical garden not just making a copy of an existing drawing but generating as many unique drawings on this theme as may be required of it. AARON has never seen a person or walked through a botanical garden but has been given knowledge about body postures and plants by means of rules.

AARON’s knowledge and the way AARON uses its knowledge are not like the knowledge that we, humans, have and use because human knowledge is based on experiencing the world, and people experience the world with their bodies, their brains, and their reproductive systems, which computers do not have.

However, just like humans, AARON’S knowledge has been acquired cumulatively. Once it understands the concept of a leaf cluster, for example, it can make use of that knowledge whenever it needs it. Plants exist for AARON in terms of their size, the thickness of limbs with respect to height, the rate at which limbs get thinner with respect to spreading, the degree of branching, the angular spread where branching occurs, and so on.

Similar principles hold for the formation of leaves and leaf clusters. By manipulating these factors, AARON is able to generate a wide range of plant types and will never draw quite the same plant twice, even when it draws a number of plants recognizably of the same type.

Besides, AARON must know what the human body consists of, what the different parts are, and how big they are in relation to each other. Then it has to know how the parts of the body are articulated and what are the types and ranges of movements at each joint.

Finally, because a coherently moving body is not merely a collection of independently moving parts, AARON has to know something about how body movements are coordinated: what the body has to do to keep its balance, for example.

Conceptually, this is not as difficult as it may seem, at least for standing positions with one or both feet on the ground. It is just a matter of keeping the center of gravity over the base and, where necessary, using the arms for achieving balanced positions.

It also has knowledge about occlusions so that a partially occluded human body might have, for example, just one arm and/or one leg visible but AARON knows that normal people have two arms and two legs and therefore when not occluded it will always draw two limbs. This means that AARON cannot “break” rules and will never “imagine” the possibility of drawing humans with one leg, for example, or other forms of abstraction. In that sense, AARON’s creativity is limited and very far from a human one.

Nevertheless AARON’s paintings have been exhibited in London’s Tate Modern and the San Francisco Museum of Modern Art. In some respects, then, AARON passes some kind of creative Turing test for its works are good enough to be exhibited alongside some of the best human artists.

Simon Colton’s Painting Fool (Colton et al., 2015) is much more autonomous than AARON. Although the software does not physically apply paint to canvas, it simulates many styles digitally, from collage to paint strokes. In Colton’s words:

The Painting Fool only needs minimal direction and can come up with its own concepts by going online for source material. The software runs its own web searches and crawls through social media websites. The idea is that this approach will let it produce art that is meaningful to the audience, because it is essentially drawing on the human experience as we act, feel and argue on the web.

For instance, in 2009, the Painting Fool produced its own interpretation of the war in Afghanistan, based on a news story. The result is a juxtaposition of Afghan citizens, explosions, and war graves.

Other examples of computational creativity applied to painting and other visual arts are the works of Karl Sims and of Jon McCormack. Karl Sims’s Reaction-Diffusion Media Wall (Sims, 2016) is based on the interactive simulation of chemicals that react and diffuse to create emergent dynamic patterns according to the reaction-diffusion equations governing biological morphogenesis. This work is exhibited at the Museum of Science in Boston. Previous works of Karl Sims include the application of evolutionary computations techniques to interactively evolved images in his Genetic Images system (Sims, 1994).

Jon McCormack also looks at how biological processes could be successfully applied to creative systems in his “Design After Nature Project” (McCormack, 2014). In another project, “Creative Ecosystems,” he looks at concepts and metaphors from biological ecosystems (McCormack and d’Inverno, 2012) as a means to enhance human creativity in the digital arts.

There are numerous other examples related to the visual arts. The reported ones are not just a representative set but, in my opinion, also the most important contributions to this field.

REFERENCES

— Cohen, H. 1995. “The further exploits of Aaron, painter.” Stanford Humanities Review 4(2): 141–158
— Colton, S. Halskov, J., Ventura, D., Gouldstone, I., Cook, M., and Pérez-Ferrer, B. 2015. “The Painting Fool sees! New projects with the automated painter.” International Conference on Computational Creativity 2015: 189–196
— Sims, K. 1994. “Evolving virtual creatures. Computer graphics.” In SIGGRAPH 94 21st International ACM Conference on Computer Graphics and Interactive Techniques. New York: ACM, 15–22
— Sims, K. 2016. “Reaction-diffusion media wall.” http://www.karlsims.com/rd-exhibit.html
— McCormack, J. 2014. “Balancing act: variation and utility in evolutionary art.” In Evolutionary and Biologically Inspired Music, Sound, Art and Design. Lecture Notes in Computer Science, Vol. 8601. Heidelberg: Springer, 26–37
— McCormack, J., and d’Inverno, M. 2012. Computers and Creativity. Heidelberg: Springer

This is one part from an article by Ramón López de Mántaras published in BBVA OpenMind with a title “Artificial Intelligence and the Arts: Toward Computational Creativity“.