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...
Virtual execution environments have become increasingly useful in system implementation, with dynamic translation techniques being an important component for performance-critical ...
Abstract--Feature selection is an important challenge in machine learning. Unfortunately, most methods for automating feature selection are designed for supervised learning tasks a...
New applications for autonomous robots bring them into the human environment where they are to serve as helpful assistants to untrained users in the home or office, or work as ca...
Abstract. An optimal probabilistic-planning algorithm solves a problem, usually modeled by a Markov decision process, by finding its optimal policy. In this paper, we study the k ...