Automated Decision Making for Safety Critical Applications

Abstract:  Building robust decision making systems is challenging, especially for safety critical systems such as unmanned aircraft and driverless cars. Decisions must be made based on imperfect information about the environment and with uncertainty about how the environment will evolve. In addition, these systems must carefully balance safety with other considerations, such as operational efficiency. Typically, the space of edge cases is vast, placing a large burden on human designers to anticipate problem scenarios and develop ways to resolve them. This talk discusses major challenges associated with ensuring computational tractability and establishing trust that our systems will behave correctly when deployed in the real world. We will outline some methodologies for addressing these challenges.

Biography:  Mykel Kochenderfer is a professor of aeronautics and astronautics at Stanford University. He is the director of the Stanford Intelligent Systems Laboratory (SISL), conducting research on advanced algorithms and analytical methods for the design of robust decision making systems. In addition, he is the director of the SAIL-Toyota Center for AI Research at Stanford and a co-director of the Center for AI Safety. He received a Ph.D. in informatics from the University of Edinburgh and B.S. and M.S. degrees in computer science from Stanford University. Prof. Kochenderfer is an author of the textbooks “Decision Making under Uncertainty: Theory and Application” and “Algorithms for Optimization”, both from MIT Press.

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Host: Paul Bogdan

Sponsored by:

Center for Cyber-Physical Systems and the Internet of Things (CCI) http://cci.usc.edu
Ming Hsieh Institute for Electrical and Computer Engineering (MHI) http://mhi.usc.edu