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@inproceedings{fremont-pldi19,
author = {Daniel J. Fremont and Tommaso Dreossi and Shromona Ghosh and Xiangyu Yue and Alberto L. Sangiovanni-Vincentelli and Sanjit A. Seshia},
title = {Scenic: A Language for Scenario Specification and Scene Generation},
booktitle = {Proceedings of the 40th annual ACM SIGPLAN conference on Programming Language Design and Implementation (PLDI)},
Year = {2019},
Month = {June},
abstract = {We propose a new probabilistic programming language for
the design and analysis of perception systems, especially
those based on machine learning. Specifically, we consider
the problems of training a perception system to handle 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
and sampling these to generate specialized training and test
sets. More generally, such languages can be used for cyberphysical systems and robotics to write environment models,
an essential prerequisite to any formal analysis. In this paper,
we focus on systems like autonomous cars whose environment is a scene, a configuration of physical objects. We design
a domain-specific language, Scenic, for describing scenarios
that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and
soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking
advantage of the structure provided by Scenic’s domainspecific syntax. Finally, we apply Scenic in a case study on a
convolutional neural network designed to detect cars in road
images, improving its performance beyond that achieved by
state-of-the-art synthetic data generation methods.},
}