Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the ...
Due to the large variation and richness of visual inputs, statistical learning gets more and more concerned in the practice of visual processing such as visual tracking and recogn...
A variety of sensors and positioning methods have been developed over the years. Most methods rely on active sensors (such as sonars or lasers) which have range and power restrict...
David P. Miller, Anne Wright, Randy Sargent, Rob C...
Markov decision processes (MDPs) are a very popular tool for decision theoretic planning (DTP), partly because of the welldeveloped, expressive theory that includes effective solu...
As more information becomes available electronically, tools for finding information of interest to users becomes increasingly important. The goal of the research described here is...
Eric Bloedorn, Inderjeet Mani, T. Richard MacMilla...