Topics

Hybrid Learning and Reasoning

Seminar an der Universität des Saarlandes, Fachrichtung Informatik, LSF 126868

The topics for seminar time slots (see schedule) are as follows. Currently assigned topics are marked in red.

Selected background literature is indicated below; topic reference papers are available in the web or on request from seminar organizers.

Topic#	Topic
1	Learning with symbolic semantic prior H. Wang, D. Dou, and D. Lowd (2016): Ontology-Based Deep Restricted Boltzmann Machine. Proc. International Conference on Database and Expert Systems Applications (DEXA) N. Phan, D. Dou, H. Wang, D. Kil, and B. Piniewski (2015): Ontology-Based Deep Learning for Human Behavior Prediction in Health Social Networks. Proc. 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics;
2	Learning with symbolic semantic prior II I. Donadello, L. Serafini & A. d’Avila Garcez (2017): Logic Tensor Networks for Semantic Image Interpretation. Proc. 26th International Joint Conference on Artificial Intelligence (IJCAI). see also: L. Serafini & A. d’Avila Garcez (2016): Learning and Reasoning with Logic Tensor Networks. Proc. Conference of the Italian Association for Artificial Intelligence. Springer
3	Learning with intermediate and derived semantic prior N. Bougie, L.K. Cheng, R. Ichise (2018): Combining Deep Reinforcement Learning with Prior Knowledge and Reasoning. ACM Applied Computing Review, 18(2):33-45
4	Learning with intermediate and derived semantic prior II R. Toro Icarte, TQ. Klassen, R. Valenzano & SA. McIlraith (2018): Teaching Multiple Tasks to an RL Agent using LTL Proc. 17th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)
5	Learning with intermediate and derived semantic prior III G. De Giacomo, L. GIocchi, M. Favorito & F. Patrizi (2019): Foundations for restraining bolts: Reinforcement learning with LTLf/LDLf restraining specifications. Proc. International Conference on Automated Planning Systems (ICAPS), 29(1):128-136
6	Learning intermediate symbolic abstraction for reasoning F. Pusse & M. Klusch (2019): Hybrid Online POMDP Planning and Deep Reinforcement Learning for Safer Self-Driving Cars. Proceedings of IEEE Intelligent Vehicles Symposium (IV).
7	Meta-reasoning for learning to reason (Learning to logically reason)) T. Rocktäschel & S. Riedel (2017): End-to-end Differentiable Proving. Proc. International Conference on Advances in Neural Information Processing Systems (NIPS).

Selected background references:

F. van Harmelen, A.T. Teije (2019). A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems.Journal of Web Engineering, 18(1-3):97-124, Rivers; also at https://arxiv.org/pdf/1905.12389
A.D. Garcez et al. (2019). Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning. arXiv preprint arXiv:1905.06088. https://arxiv.org/pdf/1905.06088
Tarek R. Besold et al. /2017): Neural-Symbolic Learning and Reasoning: A Survey and Interpretation.
https://arxiv.org/pdf/1711.03902
P. Hitzler, F. Bianchi, M. Ebrahimi & M.K. Sarker (2019): Neural-Symbolic Integration and the Semantic Web. http://www.semantic-web-journal.net/system/files/swj2188.pdf
International Workshop Series on Neural-Symbolic Learning and Reasoning (since 2005) and other related material: http://www.neural-symbolic.org/
International Workshop Series on Semantic Deep Learning (2017a, 2017b, 2018, 2019)
Deep Learning:

I.J. Goodfellow, Y. Bengio, A. Courville (2016): Deep Learning. MIT Press,
M. Nielsen (2017): Neural Networks and Deep Learning. http://neuralnetworksanddeeplearning.com/index.html
L. Deng (2014): A Tutorial Survey of Architectures, Algorithms, and Applications for Deep Learning. APSIPA Transactions on Signal and Information Processing, 3, e2.
http://deeplearning.net

Semantic Web:

P. Hitzler, M. Kroetzsch, S. Rudolph (2009): Foundations of Semantic Web Technologies. Chapman & Hall/CRC. http://www.semantic-web-book.org/page/Foundations_of_Semantic_Web_Technologies
A. Dengel (Hrsg.) (2012): Semantische Technologien - Grundlagen, Konzepte, Anwendungen. Springer