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...
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly ...
In many applications in mobile robotics, it is important for a robot to explore its environment in order to construct a representation of space useful for guiding movement. We refe...
This paper analyzes theoretically the exact sampling distribution of the particle swarm optimization (PSO) without any assumption imposed by all current analyses. The distribution...
We develop a new motion planning algorithm for a variant of a Dubins car with binary left/right steering and apply it to steerable needles, a new class of flexible beveltip medica...
Ron Alterovitz, Michael S. Branicky, Kenneth Y. Go...