This session will feature several prominent researchers involved with autonomous or cognitive robotics.
This includes, but is not limited to:

Pierre Bessière, CNRS, Grenoble University
Stephen Roberts, Oxford University, UK
Richard Dearden, University of Birmingham, UK

This special session will focus on the application of Bayesian probability theory to a variety of practical machine learning problems. Bayesian methods provide a unique opportunity to accommodate detailed information about the signal models as well as prior information about problem-specific constraints on parameter values. The recent increase in computational power as well as the development of more powerful sampling techniques have made the application of Bayesian methods to complex problems more practical than ever. This session will focus on a variety of signal processing applications as well as machine intelligence with a focus on robotics, intelligent instrumentation, experimental design, image understanding and vehicle health monitoring. There will be a live demo of a robot employing Bayesian adaptive exploration to solve a search-and-characterize problem.