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» Iterative Learning Control - Monotonicity and Optimization
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JSAC
2010
138views more  JSAC 2010»
14 years 8 months ago
Dynamic conjectures in random access networks using bio-inspired learning
—Inspired by the biological entities’ ability to achieve reciprocity in the course of evolution, this paper considers a conjecture-based distributed learning approach that enab...
Yi Su, Mihaela van der Schaar
ATMOS
2007
177views Optimization» more  ATMOS 2007»
14 years 11 months ago
Approximate dynamic programming for rail operations
Abstract. Approximate dynamic programming offers a new modeling and algorithmic strategy for complex problems such as rail operations. Problems in rail operations are often modeled...
Warren B. Powell, Belgacem Bouzaïene-Ayari
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...
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
ATAL
2003
Springer
15 years 3 months ago
Towards a pareto-optimal solution in general-sum games
Multiagent learning literature has investigated iterated twoplayer games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such ...
Sandip Sen, Stéphane Airiau, Rajatish Mukhe...