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arXiv · 1812.05556

Informing Artificial Intelligence Generative Techniques using Cognitive Theories of Human Creativity

Steve DiPaola·Liane Gabora·Graeme McCaig
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Abstract

The common view that our creativity is what makes us uniquely human suggests that incorporating research on human creativity into generative deep learning techniques might be a fruitful avenue for making their outputs more compelling and human-like. Using an original synthesis of Deep Dream-based convolutional neural networks and cognitive based computational art rendering systems, we show how honing theory, intrinsic motivation, and the notion of a 'seed incident' can be implemented computationally, and demonstrate their impact on the resulting generative art. Conversely, we discuss how explorations in deep learn-ing convolutional neural net generative systems can inform our understanding of human creativity. We conclude with ideas for further cross-fertilization between AI based computational creativity and psychology of creativity.

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Steve DiPaola, Liane Gabora, Graeme McCaig. 2019-07-05. Informing Artificial Intelligence Generative Techniques using Cognitive Theories of Human Creativity. https://doi.org/10.1016/j.procs.2018.11.024

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