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We investigate a human-machine collaborative drawing environment in which anautonomous agent sketches images while optionally allowing a user to directlyinfluence the agent's trajectory. We combine Monte Carlo Tree Search with imageclassifiers and test both shallow models (e.g. multinomial logistic regression)and deep Convolutional Neural Networks (e.g. LeNet, Inception v3). We foundthat using the shallow model, the agent produces a limited variety of images,which are noticably recogonisable by humans. However, using the deeper models,the agent produces a more diverse range of images, and while the agent remainsvery confident (99.99%) in having achieved its objective, to humans they mostlyresemble unrecognisable 'random' noise. We relate this to recent research whichalso discovered that 'deep neural networks are easily fooled' \cite{Nguyen2015}and we discuss possible solutions and future directions for the research.

Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks