Over the past decade, neural network (NN)-based controllers have demonstrated
remarkable efficacy in a variety of decision-making tasks. However, their
black-box nature and the risk of unexpected behaviors and surprising results
pose a challenge to their deployment in real-world systems with strong
guarantees of correctness and safety. We address these limitations by
investigating the transformation of NN-based controllers into equivalent soft
decision tree (SDT)-based controllers and its impact on verifiability.
Differently from previous approaches, we focus on discrete-output NN
controllers including rectified linear unit (ReLU) activation functions as well
as argmax operations. We then devise an exact but cost-effective transformation
algorithm, in that it can automatically prune redundant branches. We evaluate
our approach using two benchmarks from the OpenAI Gym environment. Our results
indicate that the SDT transformation can benefit formal verification, showing
runtime improvements of up to 21x and 2x for MountainCar-v0 and CartPole-v0,
respectively.

将基于神经网络的控制器转换为等效的软决策树控制器，以提高可验证性且减小意外行为的风险。在包含 ReLU 激活函数和 argmax 操作的离散输出 NN 控制器上，提出了一种自动剪枝的精确且高效的转换算法。以 OpenAI Gym 环境中的两个基准测试为例，结果表明 SDT 转换可以有助于形式验证，并显示其运行时在使用 MountainCar-v0 和 CartPole-v0 时分别提高了 21 倍和 2 倍。

神经网络控制器到决策树控制器的精确且具有成本效益的自动转换

Exact and Cost-Effective Automated Transformation of Neural Network  Controllers to Decision Tree Controllers

Deep neural networks have proved to be a very effective way to perform
classification tasks. They excel when the input data is high dimensional, the
relationship between the input and the output is complicated, and the number of
labeled training examples is large. But it is hard to explain why a learned
network makes a particular classification decision on a particular test case.
This is due to their reliance on distributed hierarchical representations. If
we could take the knowledge acquired by the neural net and express the same
knowledge in a model that relies on hierarchical decisions instead, explaining
a particular decision would be much easier. We describe a way of using a
trained neural net to create a type of soft decision tree that generalizes
better than one learned directly from the training data.

使用训练好的深度神经网络创建软决策树的方法，可以更好地泛化，并解释快速分类决策的过程。