6,399 research outputs found
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
Kernel Analog Forecasting: Multiscale Test Problems
Data-driven prediction is becoming increasingly widespread as the volume of
data available grows and as algorithmic development matches this growth. The
nature of the predictions made, and the manner in which they should be
interpreted, depends crucially on the extent to which the variables chosen for
prediction are Markovian, or approximately Markovian. Multiscale systems
provide a framework in which this issue can be analyzed. In this work kernel
analog forecasting methods are studied from the perspective of data generated
by multiscale dynamical systems. The problems chosen exhibit a variety of
different Markovian closures, using both averaging and homogenization;
furthermore, settings where scale-separation is not present and the predicted
variables are non-Markovian, are also considered. The studies provide guidance
for the interpretation of data-driven prediction methods when used in practice.Comment: 30 pages, 14 figures; clarified several ambiguous parts, added
references, and a comparison with Lorenz' original method (Sec. 4.5
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Learning from small data sets is critical in many practical applications
where data collection is time consuming or expensive, e.g., robotics, animal
experiments or drug design. Meta learning is one way to increase the data
efficiency of learning algorithms by generalizing learned concepts from a set
of training tasks to unseen, but related, tasks. Often, this relationship
between tasks is hard coded or relies in some other way on human expertise. In
this paper, we frame meta learning as a hierarchical latent variable model and
infer the relationship between tasks automatically from data. We apply our
framework in a model-based reinforcement learning setting and show that our
meta-learning model effectively generalizes to novel tasks by identifying how
new tasks relate to prior ones from minimal data. This results in up to a 60%
reduction in the average interaction time needed to solve tasks compared to
strong baselines.Comment: 11 pages, 7 figure
Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression
Networks of coupled dynamical systems provide a powerful way to model systems
with enormously complex dynamics, such as the human brain. Control of
synchronization in such networked systems has far reaching applications in many
domains, including engineering and medicine. In this paper, we formulate the
synchronization control in dynamical systems as an optimization problem and
present a multi-objective genetic programming-based approach to infer optimal
control functions that drive the system from a synchronized to a
non-synchronized state and vice-versa. The genetic programming-based controller
allows learning optimal control functions in an interpretable symbolic form.
The effectiveness of the proposed approach is demonstrated in controlling
synchronization in coupled oscillator systems linked in networks of increasing
order complexity, ranging from a simple coupled oscillator system to a
hierarchical network of coupled oscillators. The results show that the proposed
method can learn highly-effective and interpretable control functions for such
systems.Comment: Submitted to nonlinear dynamic
Correct-by-Construction Approach for Self-Evolvable Robots
The paper presents a new formal way of modeling and designing reconfigurable
robots, in which case the robots are allowed to reconfigure not only
structurally but also functionally. We call such kind of robots
"self-evolvable", which have the potential to be more flexible to be used in a
wider range of tasks, in a wider range of environments, and with a wider range
of users. To accommodate such a concept, i.e., allowing a self-evovable robot
to be configured and reconfigured, we present a series of formal constructs,
e.g., structural reconfigurable grammar and functional reconfigurable grammar.
Furthermore, we present a correct-by-construction strategy, which, given the
description of a workspace, the formula specifying a task, and a set of
available modules, is capable of constructing during the design phase a robot
that is guaranteed to perform the task satisfactorily. We use a planar
multi-link manipulator as an example throughout the paper to demonstrate the
proposed modeling and designing procedures.Comment: The paper has 17 pages and 4 figure
- …