Zobrazeno 1 - 10
of 145
pro vyhledávání: '"Hähner, Jörg"'
Cartesian Genetic Programming (CGP) suffers from a specific limitation: Positional bias, a phenomenon in which mostly genes at the start of the genome contribute to a program output, while genes at the end rarely do. This can lead to an overall worse
Externí odkaz:
http://arxiv.org/abs/2410.00518
Procedural knowledge describes how to accomplish tasks and mitigate problems. Such knowledge is commonly held by domain experts, e.g. operators in manufacturing who adjust parameters to achieve quality targets. To the best of our knowledge, no real-w
Externí odkaz:
http://arxiv.org/abs/2308.08371
Achieving at least some level of explainability requires complex analyses for many machine learning systems, such as common black-box models. We recently proposed a new rule-based learning system, SupRB, to construct compact, interpretable and transp
Externí odkaz:
http://arxiv.org/abs/2207.05582
In socio-technical settings, operators are increasingly assisted by decision support systems. By employing these, important properties of socio-technical systems such as self-adaptation and self-optimization are expected to improve further. To be acc
Externí odkaz:
http://arxiv.org/abs/2207.02300
While utilization of digital agents to support crucial decision making is increasing, trust in suggestions made by these agents is hard to achieve. However, it is essential to profit from their application, resulting in a need for explanations for bo
Externí odkaz:
http://arxiv.org/abs/2202.01677
Autor:
Sailer, Richard, Hähner, Jörg
Publikováno v:
IEEE LCN 2019
This paper proposes AFMT, a packet scheduling algorithm to achieve adaptive flow-aware multipath tunnelling. AFMT has two unique properties. Firstly, it implements robust adaptive traffic splitting for the subtunnels. Secondly, it detects and schedul
Externí odkaz:
http://arxiv.org/abs/2009.04579
Autor:
von Pilchau, Wenzel Pilar, Gowtham, Varun, Gruber, Maximilian, Riedl, Matthias, Koutrakis, Nikolaos-Stefanos, Tayyub, Jawad, Hähner, Jörg, Eichstädt, Sascha, Uhlmann, Eckart, Polte, Julian, Frey, Volker, Willner, Alexander
Several use cases from the areas of manufacturing and process industry, require highly accurate sensor data. As sensors always have some degree of uncertainty, methods are needed to increase their reliability. The common approach is to regularly cali
Externí odkaz:
http://arxiv.org/abs/2008.07282
We propose the SupRB learning system, a new Pittsburgh-style learning classifier system (LCS) for supervised learning on multi-dimensional continuous decision problems. SupRB learns an approximation of a quality function from examples (consisting of
Externí odkaz:
http://arxiv.org/abs/2002.10295
XCS constitutes the most deeply investigated classifier system today. It bears strong potentials and comes with inherent capabilities for mastering a variety of different learning tasks. Besides outstanding successes in various classification and reg
Externí odkaz:
http://arxiv.org/abs/2002.05628
An important component of many Deep Reinforcement Learning algorithms is the Experience Replay which serves as a storage mechanism or memory of made experiences. These experiences are used for training and help the agent to stably find the perfect tr
Externí odkaz:
http://arxiv.org/abs/2002.01370