We investigate a human-machine collaborative drawing environment in which an
autonomous agent sketches images while optionally allowing a user to directly
influence the agent's trajectory. We combine Monte Carlo Tree Search with image
classifiers and test both shallow models (e.g. multinomial logistic regression)
and deep Convolutional Neural Networks (e.g. LeNet, Inception v3). We found
that 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 remains
very confident (99.99%) in having achieved its objective, to humans they mostly
resemble unrecognisable 'random' noise. We relate this to recent research which
also discovered that 'deep neural networks are easily fooled' \cite{Nguyen2015}
and we discuss possible solutions and future directions for the research.

研究人机协作绘图环境，在其中自主代理绘制图像的同时，可选择允许用户直接影响代理的轨迹。我们结合了蒙特卡罗树搜索和图像分类器，测试了浅层模型（例如多项式逻辑回归）和深度卷积神经网络（例如 LeNet，Inception v3）。我们发现，使用浅层模型，代理会产生有限种类的图像，这些图像显然对人类来说是可识别的。然而，使用更深层次的模型，代理产生了更多样化的图像，尽管代理非常有信心（99.99％）地完成了其目标，但在人类眼中，它们大多类似于不可识别的 “随机” 噪声。这与最近的研究有关，该研究也发现 “深度神经网络很容易被愚弄”，我们讨论了可能的解决方案和未来的研究方向。