TY  - BOOK
AU  - Bergmann,Ralph
AU  - Malburg,Lukas
AU  - Rodermund,Stephanie C.
AU  - Timm,Ingo J.
TI  - KI 2022: Advances in Artificial Intelligence: 45th German Conference on AI, Trier, Germany, September 19-23, 2022, Proceedings 
T2  - Lecture Notes in Artificial Intelligence,
SN  - 9783031157912
U1  - 006.3 23
PY  - 2022///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Artificial intelligence
KW  - Computer engineering
KW  - Computer networks 
KW  - Application software
KW  - Education-Data processing
KW  - Computer science-Mathematics
KW  - Artificial Intelligence
KW  - Computer Engineering and Networks
KW  - Computer and Information Systems Applications
KW  - Computers and Education
KW  - Mathematics of Computing
N1  - An Implementation of Nonmonotonic Reasoning with System W -- Leveraging implicit gaze-based user feedback for interactive machine learning -- The Randomness of Input Data Spaces is an A Priori Predictor for Generalization -- Communicating Safety of Planned Paths via Optimally-Simple Explanations -- Assessing the Accuracy-Explainability-Cost Trade-off on Model Selection for Retail Article Categorization -- Enabling Supervised Machine Learning for SMEs through Data Pooling: A Case Study in the Service Industry -- Unsupervised Alignment of Distributional Word Embeddings. NeuralPDE: Modelling Dynamical Systems from Data -- Deep Neural Networks for Geometric Shape Deformation -- Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling -- Optimal Fixed-Premise Repairs of EL TBoxes -- Health And Habit: an Agent-based Approach -- Knowledge Graph Embeddings with Ontologies: Reification for Representing Arbitrary Relations -- Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning -- HanKA: Enriched Knowledge Used by an Adaptive Cooking Assistant -- Automated Kantian Ethics: A Faithful Implementation and Testing Framework -- PEBAM: A Profile-based Evaluation Method for Bias Assessment on Mixed Datasets
N2  - Chapter "Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com
UR  - https://doi.org/10.1007/978-3-031-15791-2
ER  -