TITLE: Optimization Strategies for Cognition and Autonomy in Mixed 
Human-Robot Teams

SPEAKER: Francesco Bullo
	 http://motion.me.ucsb.edu

JOINT WORK WITH: Vaibhav Srivastava


ABSTRACT:

We consider the optimal servicing of a queue with sigmoid server
performance. The sigmoid server performance occurs in various domains
including human decision making, visual perception, human-machine
communication and advertising response. The tasks arrive at a given rate to
the server. Each task has a deadline that is incorporated as a latency
penalty. We investigate the trade-off between the reward obtained by
processing the current task and the penalty incurred due to the tasks
waiting in the queue.  We study this optimization problem in a Markov
decision process (MDP) framework and show that the MDP formulation is
equivalent to a certainty-equivalent problem. We determine the receding
horizon servicing policy for the queue and show that the optimal policy may
drop some tasks, that is, may not process a task at all. We then develop an
adaptive policy that incorporates all the available information about the
current tasks and show that the adaptive policy improves the performance
significantly. Finally, we present a comparative study of the receding
horizon policy for the certainty-equivalent problem and the adaptive
policy. We also suggest guidelines for the design of such queues.