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IJCAI
2001
14 years 1 days ago
Approximate inference for first-order probabilistic languages
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...
Hanna Pasula, Stuart J. Russell
UAI
1996
13 years 12 months ago
Learning Bayesian Networks with Local Structure
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 ...
Nir Friedman, Moisés Goldszmidt
IJCAI
1989
13 years 11 months ago
Coping With Uncertainty in Map Learning
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...
Kenneth Basye, Thomas Dean, Jeffrey Scott Vitter
GECCO
2008
Springer
145views Optimization» more  GECCO 2008»
13 years 11 months ago
Allocation of local and global search capabilities of particle in canonical PSO
This paper analyzes theoretically the exact sampling distribution of the particle swarm optimization (PSO) without any assumption imposed by all current analyses. The distribution...
Junqi Zhang, Kun Liu, Ying Tan, Xingui He
IJRR
2008
101views more  IJRR 2008»
13 years 10 months ago
Motion Planning Under Uncertainty for Image-guided Medical Needle Steering
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...