ISSN:
1573-7462
Keywords:
locally weighted regression
;
LOESS
;
LWR
;
lazy learning
;
memory-based learning
;
least commitment learning
;
forward models
;
inverse models
;
linear quadratic regulation (LQR)
;
shifting setpoint algorithm
;
dynamic programming
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1006511328852
