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ICANNGA
2007
Springer
105views Algorithms» more  ICANNGA 2007»
15 years 9 months ago
Reinforcement Learning in Fine Time Discretization
Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet th...
Pawel Wawrzynski
CDC
2008
IEEE
15 years 9 months ago
Shannon meets Bellman: Feature based Markovian models for detection and optimization
— The goal of this paper is to develop modeling techniques for complex systems for the purposes of control, estimation, and inference: (i) A new class of Hidden Markov Models is ...
Sean P. Meyn, George Mathew
ESSMAC
2003
Springer
15 years 8 months ago
Nonlinear Predictive Control with a Gaussian Process Model
Abstract. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can h...
Jus Kocijan, Roderick Murray-Smith
AAAI
2010
15 years 4 months ago
Decision-Theoretic Control of Crowd-Sourced Workflows
Crowd-sourcing is a recent framework in which human intelligence tasks are outsourced to a crowd of unknown people ("workers") as an open call (e.g., on Amazon's Me...
Peng Dai, Mausam, Daniel S. Weld
CGF
2006
139views more  CGF 2006»
15 years 2 months ago
Pose Controlled Physically Based Motion
In this paper we describe a new method for generating and controlling physically-realistic motion of complex articulated characters. Our goal is to create motion from scratch, whe...
Raanan Fattal, Dani Lischinski