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IAT
2008
IEEE
15 years 8 months ago
Formalizing Multi-state Learning Dynamics
This paper extends the link between evolutionary game theory and multi-agent reinforcement learning to multistate games. In previous work, we introduced piecewise replicator dynam...
Daniel Hennes, Karl Tuyls, Matthias Rauterberg
GECCO
2006
Springer
188views Optimization» more  GECCO 2006»
15 years 5 months ago
Dominance learning in diploid genetic algorithms for dynamic optimization problems
This paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic environments. In this scheme, the genotype to phenotype mapping in each gene locus i...
Shengxiang Yang
112
Voted
ICONIP
2010
15 years 14 days ago
Learning Basis Representations of Inverse Dynamics Models for Real-Time Adaptive Control
Abstract. In this paper, we propose a novel approach for adaptive control of robotic manipulators. Our approach uses a representation of inverse dynamics models learned from a vari...
Yasuhito Horiguchi, Takamitsu Matsubara, Masatsugu...
NIPS
1994
15 years 3 months ago
Interference in Learning Internal Models of Inverse Dynamics in Humans
Experiments were performed to reveal some of the computational properties of the human motor memory system. We show that as humans practice reaching movements while interacting wi...
Reza Shadmehr, Tom Brashers-Krug, Ferdinando A. Mu...
ICPR
2010
IEEE
15 years 28 days ago
Learning Non-Linear Dynamical Systems by Alignment of Local Linear Models
Abstract—Learning dynamical systems is one of the important problems in many fields. In this paper, we present an algorithm for learning non-linear dynamical systems which works...
Masao Joko, Yoshinobu Kawahara, Takehisa Yairi