Exploring high-level Perspectives on Self-Configuration Capabilities of Systems
June 28, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Aleksander Lodwich
arXiv ID
1606.08906
Category
cs.AI: Artificial Intelligence
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Optimization of product performance repetitively introduces the need to make products adaptive in a more general sense. This more general idea is often captured under the term 'self-configuration'. Despite the importance of such capability, research work on this feature appears isolated by technical domains. It is not easy to tell quickly whether the approaches chosen in different technological domains introduce new ideas or whether the differences just reflect domain idiosyncrasies. For the sake of easy identification of key differences between systems with self-configuring capabilities, I will explore higher level concepts for understanding self-configuration, such as the Ξ©-units, in order to provide theoretical instruments for connecting different areas of technology and research.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Artificial Intelligence
π
π
The Cartographer
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted
Federated Machine Learning: Concept and Applications
R.I.P.
π»
Ghosted
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
R.I.P.
π»
Ghosted
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
R.I.P.
π»
Ghosted
Rainbow: Combining Improvements in Deep Reinforcement Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted