This paper proposes a new open-ended learning framework which aims at implementing an
autonomous agent using intrinsic motivations (IM) at two different levels. At the first level, the …

This paper addresses the problem of achieving lifelong open-ended learning autonomy in
robotics, and how different cognitive architectures provide functionalities that support it. To …

Autonomous open-ended learning is a relevant approach in machine learning and robotics,
allowing artificial agents to acquire a wide repertoire of goals and motor skills without the …

Compared to autonomous agent learning, lifelong agent learning tackles the additional
challenge of accumulating skills in a way favourable to long term development. What an …

An important challenge in the field of autonomous open-ended learning is the autonomous
learning of interdependent tasks, and in particular when such interdependencies are non …

Recently, AI systems have made remarkable progress in various tasks. Deep Reinforcement
Learning (DRL) is an effective tool for agents to learn policies in low-level state spaces to …

Autonomously acquiring knowledge and skills interacting with the environment is
fundamental for systems operating in real-world scenarios. While the majority of robotics …

In this work we introduce a defeasible Description Logic for abductive reasoning. Our
proposal exploits a fragment of a probabilistic extension of a Description Logic of typicality …

Open-ended learning is a core research field of developmental robotics and AI aiming to
build learning machines and robots that can autonomously acquire knowledge and skills …

Autonomous open-ended learning is a relevant approach in machine learning and robotics,
allowing the design of artificial agents able to acquire goals and motor skills without the …