Topic #

Topic

1

Informed Learning with Prior Knowledge (I)

Phan, N., et al. (2017): Ontology-based Deep Learning for Human Behavior Prediction with Explanations in Health Social Networks. Journal of Information Sciences, 384, Elsevier.  [paper]

2

Informed Learning with Prior Knowledge (II)

Marra, G., et al. (2020): Inference in Relational Neural Machines. Proceedings of 1st International Workshop on New Foundations for Human-Centered AI co-located with 24th European Conference on Artificial Intelligence (ECAI).  [paper]

3

Informed Learning with Prior Knowledge (III)

De Giacomo, G., et al. (2019): Foundations for Restraining Bolts: Reinforcement Learning with LTLf/LDLf Restraining Specifications. Proceedings of 30th International Conference on Automated Planning and Scheduling (ICAPS).  [paper]

4

Learning Logical Representations for Statistical Inferencing (I)

Cohen, W.W., et al. (2020):  Scalable Neural Methods for Reasoning with a Symbolic Knowledge Base. Proceedings of 8th International Conference of Learning Representations (ICLR). [paper]

5

Learning Logical Representations for Statistical Inferencing (II)

Almasan, P., et al. (2019): Deep Reinforcement Learning Meets Graph Neural Networks: Exploring a Routing Optimization Use Case. Journal of Computer Communications.  [paper]

6

Learning for Planning (I)

Ferber, P. et al. (2022): Neural Network Heuristic Functions for Classical Planning: Bootstrapping and Comparison to Other Methods. Proceedings of 32nd International Conference on Automated Planning and Scheduling (ICAPS).   [paper] [sup]

7

Learning for Planning (II)

Gehring, C. et al. (2022): Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators. Proceedings of 32nd International Conference on Automated Planning and Scheduling (ICAPS).  [paper]

8

Learning for Planning (III)

Pusse, F. & Klusch, M. (2019): Hybrid Online POMDP Planning and Deep Reinforcement Learning for Safer Self-Driving Cars. Proc. 30th IEEE International Intelligent Vehicles Symposium (IV), I.  [paper] [sup]

9

Explainable Learning Through Rational Reconstruction

Yang, Y., et al. (2022): LOGICDEF: An Interpretable Defense Framework Against Adversarial Examples via Inductive Scene Graph. Proceedings of AAAI Conference on Artificial Intelligence (AAAI).  [paper]