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» On an anytime algorithm for control
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CI
2007
99views more  CI 2007»
13 years 9 months ago
A Training Simulation System with Realistic Autonomous Ship Control
In this paper we present a computational approach to developing effective training systems for virtual simulation environments. In particular, we focus on a Naval simulation syste...
Monica N. Nicolescu, Ryan E. Leigh, Adam Olendersk...
ICML
2009
IEEE
14 years 10 months ago
Structure learning of Bayesian networks using constraints
This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
Cassio Polpo de Campos, Zhi Zeng, Qiang Ji
ICML
2009
IEEE
14 years 10 months ago
Learning when to stop thinking and do something!
An anytime algorithm is capable of returning a response to the given task at essentially any time; typically the quality of the response improves as the time increases. Here, we c...
Barnabás Póczos, Csaba Szepesv&aacut...
AMS
2005
Springer
107views Robotics» more  AMS 2005»
14 years 2 months ago
Elastic View Graphs: A new Framework for Sequential 3D-SLAM
This paper presents a new approach for incremental 3D SLAM from segmented range images with unknown feature association. For feature and motion tracking, an any-time interpretation...
Peter Kohlhepp, Marcus Walther
IJCAI
2007
13 years 10 months ago
A Hybridized Planner for Stochastic Domains
Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
Mausam, Piergiorgio Bertoli, Daniel S. Weld