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» Retrenchment, Refinement, and Simulation
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FLAIRS
2004
15 years 1 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
103
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CEC
2010
IEEE
15 years 24 days 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...
103
Voted
AUTOMATICA
2006
103views more  AUTOMATICA 2006»
14 years 11 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»
14 years 11 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»
14 years 11 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&...