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» Metacognitive Control and Optimal Learning
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GECCO
2006
Springer
159views Optimization» more  GECCO 2006»
15 years 1 months ago
Multi-step environment learning classifier systems applied to hyper-heuristics
Heuristic Algorithms (HA) are very widely used to tackle practical problems in operations research. They are simple, easy to understand and inspire confidence. Many of these HAs a...
Javier G. Marín-Blázquez, Sonia Schu...
92
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ATAL
2010
Springer
14 years 10 months ago
Distributed multiagent learning with a broadcast adaptive subgradient method
Many applications in multiagent learning are essentially convex optimization problems in which agents have only limited communication and partial information about the function be...
Renato L. G. Cavalcante, Alex Rogers, Nicholas R. ...
RAS
2008
80views more  RAS 2008»
14 years 9 months ago
Motion design and learning of autonomous robots based on primitives and heuristic cost-to-go
The task of trajectory design of autonomous vehicles is typically two-fold. First, it needs to take into account the intrinsic dynamics of the vehicle, which are sometimes termed ...
Keyong Li, Raffaello D'Andrea
ICFP
2008
ACM
15 years 9 months ago
Write it recursively: a generic framework for optimal path queries
Optimal path queries are queries to obtain an optimal path specified by a given criterion of optimality. There have been many studies to give efficient algorithms for classes of o...
Akimasa Morihata, Kiminori Matsuzaki, Masato Takei...
AIME
2007
Springer
15 years 3 months ago
Variable Selection for Optimal Decision Making
This paper discusses variable selection for medical decision making; in particular decisions regarding when to provide treatment and which treatment to provide. Current variable se...
Lacey Gunter, Ji Zhu, Susan Murphy