Especially in higher and further education, today’s learning more and more allows and also requires individual learning strategies. On the one hand, this development is a step towards lifelong learning and a higher personal engagement. On the other hand, many learners struggle with organizing learning processes on their own. Learning diaries were proven to be supportive for self-regulated learn...

Deep neural networks (DNNs) exploit many layers and a large number of parameters to achieve excellent performance. The training process DNN models generally handles large-scale input data with sparse features, which incurs high Input/Output (IO) cost, while some are compute-intensive. exploits distributed computing resources reduce time. While heterogeneous resources, e.g., CPUs, GPUs multiple ...

Reinforcement learning (RL) is a biologically supported learning paradigm, which allows an agent to learn through experience acquired by interaction with its environment. Its potential to learn complex action sequences has been proven for a variety of problems, such as navigation tasks. However, the interactive randomized exploration of the state space, common in reinforcement learning, makes i...

this article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for reinforcement learning agents. these definitions are used as a tool of knowledge transfer among agents. the agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. in other words, the agents are assumed t...

Due to the complexity of the data distribution problem in Distributed Database Systems, most of the proposed solutions divide the design process into two parts: the fragmentation and the allocation of fragments to the locations in the network. Here we consider the allocation problem with the possibility to replicate fragments, minimizing the total cost, which is in general NP-complete, and prop...

In this paper, we propose an information distribution system with reinforcement learning so that the information can be distributed to users with a small number of distributions. In this system, the information distributor moves along the same route and distributes the information to users by using the wireless communication technology. Here, we consider a case where the distributor would like ...

This paper introduces an integration of reinforcement learning and behavior-based control designed to produce real-time learning in situated agents. The model layers a distributed and asynchronous reinforcement learning algorithm over a learned topological map and standard behavioral substrate to create a reinforcement learning complex. The topological map creates a small and task-relevant stat...

This paper introduces an integration of reinforcement learning and behavior-based control designed to produce real-time learning in situated agents. The model layers a distributed and asynchronous reinforcement learning algorithm over a learned topological map and standard behavioral substrate to create a reinforcement learning complex. The topological map creates a small and task-relevant stat...