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» Combining Simple Models to Approximate Complex Dynamics
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IAT
2008
IEEE
14 years 2 months ago
Formalizing Multi-state Learning Dynamics
This paper extends the link between evolutionary game theory and multi-agent reinforcement learning to multistate games. In previous work, we introduced piecewise replicator dynam...
Daniel Hennes, Karl Tuyls, Matthias Rauterberg
IJRR
2006
183views more  IJRR 2006»
13 years 7 months ago
Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application
Reliable and efficient perception and reasoning in dynamic and densely cluttered environments are still major challenges for driver assistance systems. Most of today's system...
Christophe Coué, Cédric Pradalier, C...
ECAL
2007
Springer
14 years 1 months ago
Controlling an Anthropomimetic Robot: A Preliminary Investigation
The human body has a complex skeleton, giving a very high number of degrees of freedom, and is actuated by a large number of elastic elements muscles and tendons. As a consequence,...
Hugo Gravato Marques, Richard Newcombe, Owen Holla...
AAAI
1997
13 years 9 months ago
Incremental Methods for Computing Bounds in Partially Observable Markov Decision Processes
Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect ...
Milos Hauskrecht
AVSS
2006
IEEE
14 years 1 months ago
A Random Field Model for Improved Feature Extraction and Tracking
This paper presents a novel method for illuminationinvariant and contrast preserving feature extraction, aimed at improving performance of tracking under complex light condition. ...
Xiaotong Yuan, Stan Z. Li