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» Combining Simple Models to Approximate Complex Dynamics
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CA
1999
IEEE
13 years 12 months ago
Fast Synthetic Vision, Memory, and Learning Models for Virtual Humans
This paper presents a simple and efficient method of modeling synthetic vision, memory, and learning for autonomous animated characters in real-time virtual environments. The mode...
James J. Kuffner Jr., Jean-Claude Latombe
NIPS
2007
13 years 9 months ago
Random Sampling of States in Dynamic Programming
We combine three threads of research on approximate dynamic programming: sparse random sampling of states, value function and policy approximation using local models, and using lo...
Christopher G. Atkeson, Benjamin Stephens
ICPR
2002
IEEE
14 years 8 months ago
Bayesian Networks as Ensemble of Classifiers
Classification of real-world data poses a number of challenging problems. Mismatch between classifier models and true data distributions on one hand and the use of approximate inf...
Ashutosh Garg, Vladimir Pavlovic, Thomas S. Huang
JAIR
2008
93views more  JAIR 2008»
13 years 7 months ago
A Rigorously Bayesian Beam Model and an Adaptive Full Scan Model for Range Finders in Dynamic Environments
This paper proposes and experimentally validates a Bayesian network model of a range finder adapted to dynamic environments. All modeling assumptions are rigorously explained, and...
Tinne De Laet, Joris De Schutter, Herman Bruyninck...
AMS
2005
Springer
112views Robotics» more  AMS 2005»
14 years 1 months ago
Combining Learning and Programming for High-Performance Robot Controllers
Abstract. The implementation of high-performance robot controllers for complex control tasks such as playing autonomous robot soccer is tedious, errorprone, and a never ending prog...
Alexandra Kirsch, Michael Beetz