Philip Galanter

Computational Aesthetic Evaluation: Past and Future

Human creativity typically includes a self-critical aspect that guides innovation towards a productive end. This chapter offers a brief history of, and outlook for, computational aesthetic evaluation by digital systems as a contribution towards potential machine creativity. First, computational aesthetic evaluation is defined and the difficult nature of the problem is outlined. Next, a brief history of computational aesthetic evaluation is offered, including the use of formulaic and geometric theories; design principles; evolutionary systems including extensions such as coevolution, niche construction, agent swarm behaviour and curiosity; artificial neural networks and connectionist models; and complexity models. Following this historical review, a number of possible contributions towards future computational aesthetic evaluation methods are noted. Included are insights from evolutionary psychology; models of human aesthetics from psychologists such as Arnheim, Berlyne, and Martindale; a quick look at empirical studies of human aesthetics; the nascent field of neuroaesthetics; new connectionist computing models such as hierarchical temporal memory; and computer architectures for evolvable hardware. Finally, it is suggested that the effective complexity paradigm is more useful than information or algorithmic complexity when thinking about aesthetics.

Complexism and the Role of Evolutionary Art

Artists have always learned from nature. A new generation of artists is adapting the very processes of life to create exciting new works. But art is more than the creation of objects. It is also a progression of ideas with a history and a correspondence to the larger culture.

Evaluation of Creative Aesthetics

This chapter is an edited conversation on the topic of computational evaluation of artistic artefacts. The participants were Harold Cohen, Frieder Nake, David Brown, Jon McCormack, Paul Brown and Philip Galanter. It began at the Dagstuhl seminar on computers and creativity, held in Germany in 2009 and continued over a period of several months via email. The participants discuss their views on the prospects for computational evaluation of both the artistic process and the made artefact.

I'm not there: extending the range of human senses to benefit wildlife corridors

How many experiences do we miss -- either through inattention or our own limitations -- when walking through the woods or diving with scuba mask and flippers? All around us, animals communicate and perceive with senses quite different from our own that have evolved from particular needs. Just as humans have employed technology to overcome limitations of physical strength, dexterity, and distance, so can we imagine technologies that enable us to extend our senses by taking cues from birds, whales, and other animals.

Developing stylized trees and landscapes inspired by Eyvind Earle

This work uses Houdini to explore methods of generating and rendering landscapes stylized to mimic the artwork of artist Eyvind Earle (1916-2000). I present general guidelines and procedural characterizations of various trees, a forest, cliffs, mountains, an ocean, and waves. I developed methods for matching the shapes of the objects, relying on noise, L-systems, and other constraints. The graphic style places importance on the shape of the geometry, as it is rendered with simple toon-based shading techniques. Additionally, any intricate details are portrayed with point or line geometry instead of a traditional shader to mimic fine illustrative brushwork. Carefully chosen color palettes, separate shadow geometry, and level-of-detail rules also add to the likeness of Earles style.

Stylized trees and landscapes

classroom use is granted without fee provided that copies are not made or distributed for commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. SIGGRAPH 2014, August 10 – 14, 2014, Vancouver, British Columbia, Canada. 2014 Copyright held by the Owner/Author. ACM 978-1-4503-2958-3/14/08 Stylized Trees and Landscapes

Computational aesthetic evaluation: automated fitness functions for evolutionary art, design, and music

We report on a robotic system that can physically produce paintings with a wide range of artistic media such as acrylic paint on canvas. The system is composed of an articulated painting arm and a machine-learning algorithm aimed at determining a series of brushstrokes that will transfer a given electronic image onto canvas. An artist controlling the system is able to influence the resulting art piece through choice of various parameters, such as the palette, brush types and brushstroke parameters. Alternatively, an artist is able to influence the outcome through varying the algorithmic parameters and feedback of the learning algorithm itself. In these results, a genetic algorithm used a painting simulation to optimize similarity between the target and the source images.