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» Retrenchment, Refinement, and Simulation
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FLAIRS
2004
13 years 9 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
CEC
2010
IEEE
13 years 8 months ago
Multi-objective memetic evolution of ART-based classifiers
In this paper we present a novel framework for evolving ART-based classification models, which we refer to as MOME-ART. The new training framework aims to evolve populations of ART...
Rong Li, Timothy R. Mersch, Oriana X. Wen, Assem K...
AUTOMATICA
2006
103views more  AUTOMATICA 2006»
13 years 7 months ago
A universal iterative learning stabilizer for a class of MIMO systems
Design of iterative learning control (ILC) often requires some prior knowledge about a system's control matrix. In some applications, such as uncalibrated visual servoing, th...
Ping Jiang, Huadong Chen, Leon C. A. Bamforth
COMCOM
2006
126views more  COMCOM 2006»
13 years 7 months ago
Distributed and energy-efficient target localization and tracking in wireless sensor networks
In this paper, we propose and evaluate a distributed, energy-efficient, light-weight framework for target localization and tracking in wireless sensor networks. Since radio commun...
Jeongkeun Lee, Kideok Cho, Seungjae Lee, Taekyoung...
JAIR
2008
103views more  JAIR 2008»
13 years 7 months ago
ICE: An Expressive Iterative Combinatorial Exchange
We present the design and analysis of the first fully expressive, iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language (TBBL) that is co...
Benjamin Lubin, Adam I. Juda, Ruggiero Cavallo, S&...