Michael Siebers

According to our database, Michael Siebers authored at least 21 papers between 2010 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Generating contrastive explanations for inductive logic programming based on a near miss approach.

Johannes Rabold, Michael Siebers, Ute Schmid

Mach. Learn., 2022

2020

A process framework for inducing and explaining Datalog theories.

Mark Gromowski, Michael Siebers, Ute Schmid

Adv. Data Anal. Classif., 2020

2019

Please delete that! Why should I? - Explaining learned irrelevance classifications of digital objects.

Michael Siebers, Ute Schmid

Künstliche Intell., 2019

Enriching Visual with Verbal Explanations for Relational Concepts - Combining LIME with Aleph.

Johannes Rabold, Hannah Deininger, Michael Siebers, Ute Schmid

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

2018

Intentional Forgetting in Artificial Intelligence Systems: Perspectives and Challenges.

Ingo J. Timm, Steffen Staab, Michael Siebers, Claudia Schon, Ute Schmid, Kai Sauerwald, Lukas Reuter, Marco Ragni, Claudia Niederée, Heiko Maus, Gabriele Kern-Isberner, Christian Jilek, Paulina Friemann, Thomas Eiter, Andreas Dengel, Hannah Dames, Tanja Bock, Jan Ole Berndt, Christoph Beierle

Proceedings of the KI 2018: Advances in Artificial Intelligence, 2018

Was the Year 2000 a Leap Year? Step-Wise Narrowing Theories with Metagol.

Michael Siebers, Ute Schmid

Proceedings of the Inductive Logic Programming - 28th International Conference, 2018

Explaining Black-Box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules.

Johannes Rabold, Michael Siebers, Ute Schmid

Proceedings of the Inductive Logic Programming - 28th International Conference, 2018

2017

Computer Models Solving Intelligence Test Problems: Progress and Implications (Extended Abstract).

José Hernández-Orallo, Fernando Martínez-Plumed, Ute Schmid, Michael Siebers, David L. Dowe

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Requirements for a companion system to support identifying irrelevancy.

Michael Siebers, Kyra Gobel, Cornelia Niessen, Ute Schmid

Proceedings of the International Conference on Companion Technology, 2017

2016

Characterizing facial expressions by grammars of action unit sequences - A first investigation using ABL.

Michael Siebers, Ute Schmid, Dominik Seuß, Miriam Kunz, Stefan Lautenbacher

Inf. Sci., 2016

Computer models solving intelligence test problems: Progress and implications.

José Hernández-Orallo, Fernando Martínez-Plumed, Ute Schmid, Michael Siebers, David L. Dowe

Artif. Intell., 2016

Automatische Detektion von Trockenstress bei Tabakpflanzen mittels Machine-Learning-Verfahren.

Michael Siebers, Franz Uhrmann, Oliver Scholz, Christoph Stocker, Ute Schmid

Proceedings of the Informatik in der Land-, Forst- und Ernährungswirtschaft, Fokus: Intelligente Systeme, 2016

2014

Transfer of Domain Knowledge in Plan Generation: Learning Goal-dependent Annulling Conditions for Actions.

Michael Siebers

Künstliche Intell., 2014

2013

A cognitive model for predicting esthetical judgements as similarity to dynamic prototypes.

Ute Schmid, Michael Siebers, Johannes Folger, Simone Schineller, Dominik Seuß, Marius Raab, Claus-Christian Carbon, Stella J. Faerber

Cogn. Syst. Res., 2013

A machine learning approach to drought stress level classification of tobacco plants.

Christoph Stocker, Franz Uhrmann, Oliver Scholz, Michael Siebers, Ute Schmid

Proceedings of the LWA 2013. Lernen, 2013

Erkennung von Sequenzen mimischer Schmerzausdrücke durch genetische Programmierung.

Christoph Stocker, Michael Siebers, Ute Schmid

Proceedings of the LWA 2013. Lernen, 2013

2012

Applying Grammar Inference To Identify Generalized Patterns of Facial Expressions of Pain.

Ute Schmid, Michael Siebers, Dominik Seuß, Miriam Kunz, Stefan Lautenbacher

Proceedings of the Eleventh International Conference on Grammatical Inference, 2012

Semi-analytic Natural Number Series Induction.

Michael Siebers, Ute Schmid

Proceedings of the KI 2012: Advances in Artificial Intelligence, 2012

Analogical Problem Solving: Insights from Verbal Reports.

Linn Gralla, Thora Tenbrink, Michael Siebers, Ute Schmid

Proceedings of the 34th Annual Meeting of the Cognitive Science Society, 2012

2011

Combining Supervised and Unsupervised Learning - An Ensemble Learning Framework Proposal.

Michael Siebers

Proceedings of the Report of the symposium "Lernen, 2011

2010

Interleaving Forward Backward Feature Selection.

Michael Siebers, Ute Schmid

Proceedings of the KDIR 2010, 2010