Artificial neural networks (ANNs) can be employed as controllers for robotic agents. Their
structure is often complex, with many neurons and connections, especially when the robots …

Voxel-based Soft Robots (VSRs) are a form of modular soft robots, composed of several
deformable cubes, ie, voxels. Each VSR is thus an ensemble of simple agents, namely the …

According to Hebbian theory, synaptic plasticity is the ability of neurons to strengthen or
weaken the synapses among them in response to stimuli. It plays a fundamental role in the …

Artificial neural networks (ANNs) are commonly used for controlling robotic agents. For
robots with many sensors and actuators, ANNs can be very complex, with many neurons …

In this work, we argue that the search for Artificial General Intelligence should start from a
much lower level than human-level intelligence. The circumstances of intelligent behavior in …

Up to now, modern machine learning (ML) has been based on approximating big data sets
with high-dimensional functions, taking advantage of huge computational resources. We …

The boom of artificial intelligence in the past decade is owed to the research and
development of deep learning and moreover, that of accessible deep learning. But the goal …

We report a bio-inspired framework for training a neural network through reinforcement
learning to induce high level functions within the network. Based on the interpretation that …

Abstract The field of Evolutionary Robotics (ER) is concerned with the evolution of artificial
agents---robots. Albeit groundbreaking, progress in the field has recently stagnated. In the …

This thesis explores the properties of a novel framework aspiring to achieve General
Intelligence via Neuroevolution, called 'Neuroevolution of Artificial General Intelligence …