---
res:
bibo_abstract:
- Quantifying behaviors of robots which were generated autonomously from task-independent
objective functions is an important prerequisite for objective comparisons of
algorithms and movements of animals. The temporal sequence of such a behavior
can be considered as a time series and hence complexity measures developed for
time series are natural candidates for its quantification. The predictive information
and the excess entropy are such complexity measures. They measure the amount of
information the past contains about the future and thus quantify the nonrandom
structure in the temporal sequence. However, when using these measures for systems
with continuous states one has to deal with the fact that their values will depend
on the resolution with which the systems states are observed. For deterministic
systems both measures will diverge with increasing resolution. We therefore propose
a new decomposition of the excess entropy in resolution dependent and resolution
independent parts and discuss how they depend on the dimensionality of the dynamics,
correlations and the noise level. For the practical estimation we propose to use
estimates based on the correlation integral instead of the direct estimation of
the mutual information based on next neighbor statistics because the latter allows
less control of the scale dependencies. Using our algorithm we are able to show
how autonomous learning generates behavior of increasing complexity with increasing
learning duration.@eng
bibo_authorlist:
- foaf_Person:
foaf_givenName: Georg S
foaf_name: Martius, Georg S
foaf_surname: Martius
foaf_workInfoHomepage: http://www.librecat.org/personId=3A276B68-F248-11E8-B48F-1D18A9856A87
- foaf_Person:
foaf_givenName: Eckehard
foaf_name: Olbrich, Eckehard
foaf_surname: Olbrich
bibo_doi: 10.3390/e17107266
bibo_issue: '10'
bibo_volume: 17
dct_date: 2015^xs_gYear
dct_language: eng
dct_publisher: Multidisciplinary Digital Publishing Institute@
dct_title: Quantifying emergent behavior of autonomous robots@
...