RI: Small: Reasoning About the Behavior of Artificial Intelligence Systems

该项目旨在开发一个全面和原则性的框架，用于对人工智能(AI)系统的行为进行推理，其中包括解释它们的决策，评估它们的健壮性，并为它们的行为提供正式保证。这些考虑对于人工智能系统在现实世界应用中的成功至关重要，例如自动驾驶汽车的安全。该项目特别关注机器学习系统，这些系统是从数据中学习的，具有固有的数字性质，这导致它们的不透明性和对其行为的推理困难。该项目基于一个基本的观察：尽管机器学习系统本质上是数字的，但它们经常实现符号决策功能。该项目的具体目标是(1)将数字机器学习系统编译成等价的、符号的和易处理的系统，然后(2)通过对编译后的符号版本的操作来推理机器学习系统的行为。这使人们能够从经典人工智能和更广泛的计算机科学中引入丰富的技术来解决这一重要问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The project aims to develop a comprehensive and principled framework for reasoning about the behavior of artificial intelligence (AI) systems, which includes explaining their decisions, assessing their robustness and providing formal guarantees on their behavior. These considerations are critical to the success of AI systems in real-world applications, such as the safety of self-driving cars. The project is particularly concerned with machine learning systems, which are learned from data and have an inherent numeric nature that contributes to their opacity and the difficulty in reasoning about their behavior. The project is based on a fundamental observation: Even though machine learning systems are numeric in nature, they often implement symbolic decision functions. The project will specifically aim to (1) compile the numeric machine learning systems into equivalent, symbolic and tractable systems, and then (2) reason about the behavior of the machine learning systems by operating on their compiled symbolic versions. This allows one to bring in a wealth of techniques from classical AI, and computer science more broadly, to bear on this important problem.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.