In this paper, we tackle learning in distributed systems and the fact that learning does not necessarily involve the participation of agents directly in the inductive process itse...
— We present an algorithm that probabilistically covers a bounded region of the state space of a nonlinear system with a sparse tree of feedback stabilized trajectories leading t...
Recent work on online auctions for digital goods has explored the role of optimal stopping theory — particularly secretary problems — in the design of approximately optimal on...
Mohammad Taghi Hajiaghayi, Robert D. Kleinberg, Tu...
The use of large quantities of common sense has long been thought to be critical to the automated understanding of the world. To this end, various groups have collected repositori...
William Pentney, Ana-Maria Popescu, Shiaokai Wang,...
This paper presents two new approaches to decomposing and solving large Markov decision problems (MDPs), a partial decoupling method and a complete decoupling method. In these app...