Recent success of machine learning in many domains has been overwhelming, which often
leads to false expectations regarding the capabilities of behavior learning in robotics. In this …

Motivated by the observation that most neural architecture search (NAS) methods are time
consuming because a “training process” is required to evaluate each searched neural …

As the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of
power load data to be analyzed at the edge, which create conditions for edge calculation …

We examine two methods which are used to deal with complex machine learning problems:
compressed sensing and model compression. We discuss both methods in the context of …

Classical conditioning (conventionally modeled as correlation-based learning) and operant
conditioning (conventionally modeled as reinforcement learning or reward-based learning) …

We present a novel method of reducing the training time by learning parameters of a model
at hand in compressed parameter space. In compressed parameter space the parameters of …

In this paper, we describe possible methods of solving two problems encountered in
evolutionary robotics, while applying neuroevolutionary methods to evolve controllers for …

Evolving a monolithic solution for complex robotic problems is hard. One of the reasons for
this is the difficulty of defining a global fitness function that leads to a solution with desired …

In this contribution we present an extension of a neuroevolutionary method called
Evolutionary Acquisition of Neural Topologies (EANT)[11] that allows the evolution of …

Ich versichere, die Diplomarbeit oder den von mir zu verantwortenden Teil einer
Gruppenarbeit1 ohne fremde Hilfe angefertigt zu haben. Ich habe keine anderen als die …