Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Plan recognition has traditionally been developed for logically encoded application domains with a focus on logical reasoning. In this paper, we present an integrated plan-recogni...
Probabilistic Roadmaps (PRM) are a commonly used class of algorithms for robot navigation tasks where obstacles are present in the environment. We examine the situation where the ...
The paper summarizes some important results at the intersection of the fields of Bayesian statistics and stochastic simulation. Two statistical analysis issues for stochastic sim...
Mobile medical sensors promise to provide an efficient, accurate, and economic way to monitor patients’ health outside the hospital. Patient authentication is a necessary secur...
Janani C. Sriram, Minho Shin, Tanzeem Choudhury, D...