You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
37 lines
2.0 KiB
37 lines
2.0 KiB
entries:
|
|
10.1371journal.pone.0063400:
|
|
abstract: <p>Information theory is a powerful tool to express principles to
|
|
drive autonomous systems because it is domain invariant and allows for
|
|
an intuitive interpretation. This paper studies the use of the predictive
|
|
information (PI), also called excess entropy or effective measure complexity,
|
|
of the sensorimotor process as a driving force to generate behavior. We
|
|
study nonlinear and nonstationary systems and introduce the time-local
|
|
predicting information (TiPI) which allows us to derive exact results
|
|
together with explicit update rules for the parameters of the controller
|
|
in the dynamical systems framework. In this way the information principle,
|
|
formulated at the level of behavior, is translated to the dynamics of
|
|
the synapses. We underpin our results with a number of case studies with
|
|
high-dimensional robotic systems. We show the spontaneous cooperativity
|
|
in a complex physical system with decentralized control. Moreover, a jointly
|
|
controlled humanoid robot develops a high behavioral variety depending
|
|
on its physics and the environment it is dynamically embedded into. The
|
|
behavior can be decomposed into a succession of low-dimensional modes
|
|
that increasingly explore the behavior space. This is a promising way
|
|
to avoid the curse of dimensionality which hinders learning systems to
|
|
scale well.</p>
|
|
author:
|
|
- first: Georg
|
|
last: Ay
|
|
middle: Martius AND Ralf Der AND Nihat
|
|
doi: 10.1371/journal.pone.0063400
|
|
journal: PLoS ONE
|
|
month: '05'
|
|
number: '5'
|
|
pages: e63400
|
|
publisher: Public Library of Science
|
|
title: Information Driven Self-Organization of Complex Robotic Behaviors
|
|
type: article
|
|
url: http://dx.doi.org/10.1371%2Fjournal.pone.0063400
|
|
volume: '8'
|
|
year: '2013'
|