Yet, the few existing models considering learning effects in scheduling concentrate on learning-by-doing (autonomous learning). But recent contributions to the literature on learning in manufacturing organizations emphasize the important impact of proactive investments in technological knowledge on the learning rate (induced learning). In the present paper, we focus on a scheduling problem wher...

In recent years computer science courses have become more and more common in science and engineering degrees. However, lecturers face a complex task when teaching this subject: students consider the subject to be unrelated to their core interests and often feel uncomfortable when facing computational concepts for the first time. A non-traditional approach might help students to overcome their d...

Recently there has been great advance in building humanoid robots and other autonomous systems. Acquiring new knowledge through interactive learning mechanisms is a key ability for such systems in a natural environment. In recent and ongoing work we focus on approaches for natural learning that enables an autonomous system, such as a humanoid robot, to acquire new information through multimodal...

Recently there has been great advance in building humanoid robots and other autonomous systems. Acquiring new knowledge through interactive learning mechanisms is a key ability for such systems in a natural environment. In recent and ongoing work we focus on approaches for natural learning that enables an autonomous system, such as a humanoid robot, to acquire new information through multimodal...

In many applications the performance of learned robot controllers drags behind those of the respective hand-coded ones. In our view, this situation is caused not mainly by deficiencies of the learning algorithms but rather by an insufficient embedding of learning in robot control programs. This paper presents a case study in which ROLL, a robot control language that allows for explicit represen...

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes, but it has not yet been successfully used for automotive applications. There has recently been a revival of interest in the topic, however, driven by the ability of deep learning algorithms to learn good representations of...

In this paper, we extend the autonomous robot control and plan language RPL with constructs for specifying experiences, control tasks, learning systems and their parameterization, and exploration strategies. Using these constructs, the learning problems can be represented explicitly and transparently and become executable. With the extended language we rationally reconstruct parts of the AGILO ...

Machine learning, a cornerstone of intelligent systems, has typically been studied in the context of specific tasks, including clustering (unsupervised learning), classification (supervised learning), and control (reinforcement learning). This paper presents a learning architecture within which a universal adaptation mechanism unifies a rich set of traditionally distinct learning paradigms, inc...

In shared autonomy, user input and robot autonomy are combined to control a robot to achieve a goal. One often used strategy considers the user and autonomous system as independent decision makers, combining the output of these two entities. However, recent work has shown that this can lead to poor performance, suggesting the need to incorporate user models into the autonomous decision maker. A...