摘要：Algorithmic Improvisation, also called control improvisation or controlled improvisation, is a new framework for automatically synthesizing systems with specified random but controllable behavior. In this talk, I will present the theory of algorithmic improvisation and show how it can be used in a wide variety of applications where randomness can provide variety, robustness, or unpredictability while guaranteeing safety or other properties. Applications demonstrated to date include robotic surveillance, software fuzz testing, music improvisation, human modeling, generating test cases for simulating cyber-physical systems, and generation of synthetic data sets to train and test machine learning algorithms. In this talk, I will particularly focus on applications to the design of intelligent autonomous systems, presenting work on randomized planning for robotics and a domain-specific probabilistic programming language for the design and analysis of learning-based autonomous systems.
