Scenic: A Language for Scenario Specification and Data Generation

Understand

We propose a new probabilistic programming language for the design and analysis of cyber-physical systems, especially those based on machine learning.

• Specifically, we consider the problems of training a system to be robust to rare events, testing its performance under different conditions, and debugging failures.
• We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs, then sampling these to generate specialized training and test data.
• More generally, such languages can be used to write environment models, an essential prerequisite to any formal analysis.