In this work, we investigate the problem of revealing the functionality of a black-box agent. Notably, we are interested in the interpretable and formal description of the behavior of such an agent. Ideally, this description would take the form of a program written in a high-level language. This task is also known as reverse engineering and plays a pivotal role in software engineering, computer security, but also most recently in interpretability. In contrast to prior work, we do not rely on privileged information on the black box, but rather investigate the problem under a weaker assumption of having only access to inputs and outputs of the program. We approach this problem by iteratively refining a candidate set using a generative neural program synthesis approach until we arrive at a functionally equivalent program. We assess the performance of our approach on the Karel dataset. Our results show that the proposed approach outperforms the state-of-the-art on this challenge by finding a functional equivalent program in 78% of cases -- even exceeding prior work that had privileged information on the black-box.

中文翻译：

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IReEn：通过神经程序综合对黑盒函数进行迭代逆向工程

在这项工作中，我们研究了揭示黑盒代理功能的问题。值得注意的是，我们对这种代理行为的可解释和正式描述感兴趣。理想情况下，这种描述将采用用高级语言编写的程序的形式。这项任务也称为逆向工程，在软件工程、计算机安全以及最近的可解释性方面发挥着举足轻重的作用。与之前的工作相比，我们不依赖于黑匣子上的特权信息，而是在只能访问程序的输入和输出的较弱假设下研究问题。我们通过使用生成性神经程序综合方法迭代改进候选集来解决这个问题，直到我们得到一个功能等效的程序。我们评估我们的方法在 Karel 数据集上的性能。我们的结果表明，通过在 78% 的情况下找到功能等效的程序，所提出的方法在这一挑战中的表现优于最先进的方法——甚至超过了在黑盒上拥有特权信息的先前工作。