Topics

Hybrid Learning and Reasoning

Seminar an der Universität des Saarlandes, Fachrichtung Informatik, LSF 133348

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	From symbols to data and back T. Thanapalasingam, L. van Berkel, P. Bloem & P. Groeth (2021): Relational Graph Convolutional Networks: A Closer Look. arXiv :2107.10015. Code
2	Informed learning with prior knowledge I G. Marra, M. Diligenti, F. Giannini, M. Gori & M. Maggini (2020): Relational Neural Machines. Proceedings of 24th European Conference on Artificial Intelligence (ECAI), IOS
3	Informed learning with prior knowledge II L. D. Liello, P. Ardino, J. Gobbi, P. Morettin, S. Teso & A. Passerini (2020): Efficient Generation of Structured Objects with Constrained Adversarial Networks. Proceedings of NeurIPS Symposium on Advances in neural information processing systems & Supplemental material.
4	Informed learning with prior knowledge III L. Illanes, RT. Yan, SA. Icarte, SA. McIlraith (2020): Symbolic Plans as High-Level Instructions for Reinforcement Learning. Proceedings of AAAI 30th International Conference on Automated Planning and Scheduling (ICAPS), AAAI
5	Informed learning with prior knowledge IV W. Zhang, B. Paudel, L. Wang, J. Chen, H. Zhu, W. Zhang, A. Bernstein, and H. Chen (2019): Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning. Proceedings of World Wide Web Conference. ACM
6	Learning logical representations for statistical inferencing I P. Almasan, J. Suárez-Varela, A. Badia-Sampera, K. Rusek, P. Barlet-Ros & A. Cabellos-Aparicio (2020): Deep Reinforcement Learning Meets Graph Neural Networks: Exploring a Routing Optimization Use Case. arXiv:1910.07421.
7	Learning logical representations for statistical inferencing II P. Hart & A. Knoll (2020): Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments. Proceedings of IEEE Intelligent Vehicles Symposium (IV), IEEE
8	Learning intermediate abstraction for reasoning I 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), IEEE; code
9	Learning intermediate abstraction for reasoning II A. Micheli & A. Valentini (2021): Synthesis of Search Heuristics for Temporal Planning via Reinforcement Learning. Proceedings of AAAI Conference on Artificial Intelligence (AAAI).

Selected background references:

van Bekkum, M., de Boer, M., van Harmelen, F. et al. (2021):Modular design patterns for hybrid learning and reasoning systems: A taxonomy, patterns and use cases. Journal of Applied Intelligence.
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