10 citations · 13 across the 6 of their papers we have counts for
6 papers
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation
Simon Reichhuber, Sven Tomforde
Intelligent systems have the ability to improve their behaviour over time taking observations, experiences or explicit feedback into account. Traditional approaches separate the le…
Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning
Mirko D'Angelo, Sona Ghahremani, Simos Gerasimou +4
Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multi-dimensional and complex design space. Data-driven approaches for C…
Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields
Tom Hanika, Marek Herde, Jochen Kuhn +8
The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans i…
On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes
Stefan Rudolph, Sven Tomforde, Jörg Hähner
Self-adaptation has been proposed as a mechanism to counter complexity in control problems of technical systems. A major driver behind self-adaptation is the idea to transfer tradi…
Self-Adaptation of Activity Recognition Systems to New Sensors
David Bannach, Martin Jänicke, Vitor F. Rey +3
Traditional activity recognition systems work on the basis of training, taking a fixed set of sensors into account. In this article, we focus on the question how pattern recognitio…
Organic Computing in the Spotlight
Sven Tomforde, Bernhard Sick, Christian Müller-Schloer
Organic Computing is an initiative in the field of systems engineering that proposed to make use of concepts such as self-adaptation and self-organisation to increase the robustnes…