A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
The Virtual UNR Campus (VCam) presented in this paper is an interactive environment where users explore a 3D representation of the University of Nevada, Reno (UNR) campus. In esse...
Sergiu Dascalu, Frederick C. Harris Jr., Matthew K...
The problem of deriving joint policies for a group of agents that maximize some joint reward function can be modeled as a decentralized partially observable Markov decision proces...
Ranjit Nair, Milind Tambe, Makoto Yokoo, David V. ...
The ability to provide uniform shared-memory access to a significant number of processors in a single SMP node brings us much closer to the ideal PRAM parallel computer. In this pa...
David A. Bader, Ajith K. Illendula, Bernard M. E. ...