Friday, September 29, 2017
11 AM - 12 PM
In the stochastic multi-armed bandit problem, the objective is to optimize accumulated reward over a sequence of choices, which creates a tension between choosing the most rewarding among known options (exploitation) and choosing poorly known but potentially more rewarding options (exploration). I will discuss results from multi-armed bandit experiments with human participants and features of human decision-making captured by a model that relies on Bayesian inference, confidence bounds, and Boltzmann action selection. I will discuss how multi-armed bandit framework bridges macroscopic and microscopic foraging models in ecology. Finally, I will discuss some results on distributed cooperative decision-making in multi-player multi-armed bandit problems.
Vaibhav Srivastava received the B.Tech. degree (2007) in mechanical engineering from the Indian Institute of Technology Bombay, Mumbai, India; the M.S. degree in mechanical engineering (2011), the M.A. degree in statistics (2012), and and the Ph.D. degree in mechanical engineering (2012) from the University of California at Santa Barbara, Santa Barbara, CA.
Dr Srivastava is currently an Assistant Professor with the Electrical and Computer Engineering, Michigan State University. He served as a Lecturer and Associate Research Scholar with the Mechanical and Aerospace Engineering Department, Princeton University, Princeton, NJ from 2013-2016. He received the best paper award (as coauthor) at the 2014 European Control Conference. His research interests include shared human-autonomous systems and networked multi-agent systems.
Dr. H. Metin Aktulga