198 research outputs found
Simple nonlinear models suggest variable star universality
Dramatically improved data from observatories like the CoRoT and Kepler
spacecraft have recently facilitated nonlinear time series analysis and
phenomenological modeling of variable stars, including the search for strange
(aka fractal) or chaotic dynamics. We recently argued [Lindner et al., Phys.
Rev. Lett. 114 (2015) 054101] that the Kepler data includes "golden" stars,
whose luminosities vary quasiperiodically with two frequencies nearly in the
golden ratio, and whose secondary frequencies exhibit power-law scaling with
exponent near -1.5, suggesting strange nonchaotic dynamics and singular
spectra. Here we use a series of phenomenological models to make plausible the
connection between golden stars and fractal spectra. We thereby suggest that at
least some features of variable star dynamics reflect universal nonlinear
phenomena common to even simple systems.Comment: 9 pages, 9 figures, accepted for publication in Physica
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
Research in Artificial Intelligence (AI) has focused mostly on two extremes:
either on small improvements in narrow AI domains, or on universal theoretical
frameworks which are usually uncomputable, incompatible with theories of
biological intelligence, or lack practical implementations. The goal of this
work is to combine the main advantages of the two: to follow a big picture
view, while providing a particular theory and its implementation. In contrast
with purely theoretical approaches, the resulting architecture should be usable
in realistic settings, but also form the core of a framework containing all the
basic mechanisms, into which it should be easier to integrate additional
required functionality.
In this paper, we present a novel, purposely simple, and interpretable
hierarchical architecture which combines multiple different mechanisms into one
system: unsupervised learning of a model of the world, learning the influence
of one's own actions on the world, model-based reinforcement learning,
hierarchical planning and plan execution, and symbolic/sub-symbolic integration
in general. The learned model is stored in the form of hierarchical
representations with the following properties: 1) they are increasingly more
abstract, but can retain details when needed, and 2) they are easy to
manipulate in their local and symbolic-like form, thus also allowing one to
observe the learning process at each level of abstraction. On all levels of the
system, the representation of the data can be interpreted in both a symbolic
and a sub-symbolic manner. This enables the architecture to learn efficiently
using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl
A Posture Sequence Learning System for an Anthropomorphic Robotic Hand
The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator
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