Keywords: Agent-based Art, Artbooks, Behavior Aesthetics, Deep Learning, Generative Literature, Long Short-Term Memory, Machine Learning, Natural Language Processing, Neural Networks.
This paper describes an artbook created through the adaptive behavior of a deep learning neural network computational agent as it “reads” a novel. Through this process, the agent
builds a model of the syntactic and stylistic principles behind the original text and uses this model to generate new, unforeseen content. A limited set of unique printed copies of the
artbook are generated through this process. Each unique edition of the work thus embodies the learning process of the agent as it goes through the adaptive process, one wherein the agent begins from a state of randomness and gradually refines its output as it reads the novel. I examine the text through an analysis of generated excerpts, discussing how they reveal the behavior of the system as an adaptive agent. Practical and theoretical implications are discussed in the context of generative literature, machine learning, and behavior aesthetics.
- Sofian Audry, School of Computing and Information Science University of Maine, Orono, ME, USA
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