Automated Decision Making for Safety Critical Applications

Building robust decision making systems for autonomous systems is challenging. 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 and point to some research applications that can serve as inspiration for building safer systems.

Speaker: Mykel Hochenderfer, Stanford University

Attend in person or online (See weblink)