Algorithmic Improvisation for Dependable and Secure Autonomy

Algorithmic Improvisation, also called control improvisation, is a new framework for automatically synthesizing systems with 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, 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 and a domain-specific probabilistic programming language.

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Speaker Bios

Sanjit A. Seshia is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received an M.S. and Ph.D. in Computer Science from Carnegie Mellon University, and a B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Bombay. His research interests are in formal methods for dependable and secure computing, with a current focus on the areas of cyber-physical systems, computer security, and robotics. He has made pioneering contributions to the areas of satisfiability modulo theories (SMT), SMT-based verification, and inductive program synthesis. He is co-author of a widely-used textbook on embedded, cyber-physical systems and has led the development of technologies for cyber-physical systems education based on formal methods. His awards and honors include a Presidential Early Career Award for Scientists and Engineers (PECASE), an Alfred P. Sloan Research Fellowship, and the Frederick Emmons Terman Award for contributions to electrical engineering and computer science education. He is a Fellow of the IEEE.