NeSy2023
Programme outline

Programme outline

Accepted papers

NeSy 2023 track

1. Maurizio Proietti and Francesca Toni. A roadmap for neuro-argumentative learning

2. Ouns El Harzli, Samy Badreddine and Tarek Besold. What's Wrong with Gradient-based Complex Query Answering?

3. Kwun Ho Ngan, James Phelan, Esma Mansouri-Benssassi, Joe Townsend and Artur d'Avila Garcez. Closing the Neural-Symbolic Cycle: Knowledge Extraction, User Intervention and Distillation from Convolutional Neural Networks

4. Luca Salvatore Lorello and Marco Lippi. The Challenge of Learning Symbolic Representations

5. Moa Johansson and Nicholas Smallbone. Exploring Mathematical Conjecturing with Large Language Models

6. Mattijs Baert, Sam Leroux and Pieter Simoens. Learning Logic Constraints From Demonstration (short paper)

7. Fernando Zhapa-Camacho and Robert Hoehndorf. From axioms over graphs to vectors, and back again: evaluating the properties of graph-based ontology embeddings

8. Jingyuan Sha, Hikaru Shindo, Kristian Kersting and Devendra Singh Dhami. Neural-Symbolic Predicate Invention: Learning Relational Concepts from Visual Scenes

9. Vitor Horta, Alessandra Mileo, Maarten Stol and Robin Sobczyk. Semantic Interpretability of Convolutional Neural Networks by Taxonomy Extraction

10. Davide Beretta, Stefania Monica and Federico Bergenti. Preliminary results on a state-driven method for rule construction in neural-symbolic reinforcement learning

11. Thomas Eiter, Nelson Higuera and Johannes Oetsch. A Modular Neurosymbolic Approach for Visual Graph Question Answering

12. Jędrzej Potoniec. Is the proof length a good indicator of hardness for reason-able embeddings?

13. Emanuele Marconato, Stefano Teso and Andrea Passerini. Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations

14. Rory Ward, Muhammad Jaleed Khan, Edward Curry and John Breslin. Knowledge-Guided Colorization: Overview, Prospects and Challenges

15. Gaia Saveri and Luca Bortolussi. Towards Invertible Semantic-Preserving Embeddings of Logical Formulae

16. Matthew Brown, M. Wasil Wahi-Anwar, Youngwon Choi, Morgan Daly, Liza Shrestha, Koon-Pong Wong, Jonathan Goldin and Dieter Enzmann. Implementing trustworthy AI in real-world medical imaging using the SimpleMind software environment

17. Kristian Hammond and David Leake. Large Language Models Need Symbolic AI

18. Sofoklis Kyriakopoulos and Artur S. d'Avila Garcez. Continual Reasoning: Non-monotonic Reasoning in Neurosymbolic AI using Continual Learning

19. Lia Morra, Alberto Azzari, Letizia Bergamasco, Marco Braga, Luigi Capogrosso, Federico Delrio, Giuseppe Di Giacomo, Simone Eiraudo, Giorgia Ghione, Rocco Giudice, Alkis Koudounas, Luca Piano, Daniele Rege Cambrin, Matteo Risso, Marco Rondina, Alessandro Sebastien Russo, Marco Russo, Francesco Taioli, Lorenzo Vaiani and Chiara Vercellino. Designing Logic Tensor Networks for Visual Sudoku puzzle classification

20. Roxana Pop and Egor V. Kostylev. Inductive Future Time Prediction on Temporal Knowledge Graphs with Interval Time

21. Alberto Speranzon, Christian H. Debrunner, David Rosenbluth, Mauricio Castillo-Effen, Anthony R. Nowicki, Kevin Alcedo and Andrzej Banaszuk. Challenge Problems in Developing a NS OODA Loop

22. Johanna Ott, Arthur Ledaguenel, Celine Hudelot and Mattis Hartwig. How to Think About Benchmarking Neurosymbolic AI?

23. Elena Umili, Francesco Argenziano, Aymeric Barbin and Roberto Capobianco. Visual Reward Machines

24. Marc Otto, Octavio Arriaga, Chandandeep Singh, Jichen Guo and Frank Kirchner. PhysWM: Physical World Models for Robot Learning

25. Michael Hersche, Zuzanna Opala, Geethan Karunaratne, Abu Sebastian and Abbas Rahimi. Decoding Superpositions of Bound Symbols Represented by Distributed Representations

26. Flavio Petruzzellis, Alberto Testolin and Alessandro Sperduti. A hybrid system for systematic generalization in simple arithmetic problems

27. Francesco S. Carzaniga, Michael Hersche, Kaspar Schindler and Abbas Rahimi. VSA-based positional encoding can replace recurrent networks in emergent symbol binding

28. David Herron, Ernesto Jimenez-Ruiz and Tillman Weyde. On the benefits of OWL-based knowledge graphs for neural-symbolic systems

29. Katrin Schreiberhuber, Marta Sabou, Fajar J. Ekaputra, Peter Knees, Peb Ruswono Aryan, Alfred Einfalt and Ralf Mosshammer. Causality prediction with neural-symbolic systems: A case study in smart grids

30. Manuel Eberhardinger, Johannes Maucher and Setareh Maghsudi. Towards Explainable Decision Making with Neural Program Synthesis and Library Learning

31. Mihaela C. Stoian, Eleonora Giunchiglia and Thomas Lukasiewicz. Exploiting t-norms for deep learning in autonomous driving

32. Mouloud Iferroudjene, Victor Charpenay and Antoine Zimmermann. FB15k-CVT: A challenging dataset for knowledge graph embedding models

33. Abhinav Thakur, Filip Ilievski, Hong-An Sandlin, Zhivar Sourati, Luca Luceri, Riccardo Tommasini and Alain Mermoud. Explainable Classification of Internet Memes

Recently-published papers track

1. Emanuele Marconato, Andrea Passerini and Stefano Teso. GlanceNets: Interpretable, Leak-proof Concept-based Models

2. Cristina Cornelio, Jan Stuemer, Shell Xu Hu and Timothy Hospedales. Learning Where and When to Reason in Neuro-Symbolic Inference

3. Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck and Antonio Vergari. Semantic Probabilistic Layers for Neuro-Symbolic Learning

4. Lucile Dierckx, Rosana Veroneze and Siegfried Nijssen. RL-Net: Interpretable Rule Learning with Neural Networks

5. Alessandro Oltramari. Generalizable Neuro-Symbolic Systems for Commonsense Question Answering

6. Alessandro Daniele, Tommaso Campari, Sagar Malhotra and Luciano Serafini. Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions

7. Daniel Silver and Tom Mitchell. The Roles of Symbols in Neural-based AI: They are Not What You Think!

8. Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio, Frederic Precioso, Mateja Jamnik and Giuseppe Marra. Interpretable Neural-Symbolic Concept Reasoning

9. Anna Breit, Laura Waltersdorfer, Fajar J. Ekaputra, Marta Sabou, Andreas Ekelhart, Andreea Iana, Heiko Paulheim, Jan Portisch, Artem Revenko, Frank Van Harmelen and Annette ten Teije. Combining Machine Learning and Semantic Web: A Systematic Mapping Study

10. Michael Hersche, Mustafa Zeqiri, Luca Benini, Abu Sebastian and Abbas Rahimi. Solving Raven's Progressive Matrices via a Neuro-vector-symbolic Architecture

11. Michael Akintunde, Elena Botoeva, Panagiotis Kouvaros and Alessio Lomuscio. Verifying Strategic Abilities of Neural-symbolic Multi-agent Systems

12. Wen-Chi Yang, Giuseppe Marra, Gavin Rens and Luc De Raedt. Safe Reinforcement Learning via Probabilistic Logic Shields

13. N'Dah Jean Kouagou, Stefan Heindorf, Caglar Demir and Axel-Cyrille Ngonga Ngomo. Neural Class Expression Synthesis

14. Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Marco Gori, Pietro Lio, Marco Maggini and Stefano Melacci. Logic Explained Networks

15. Eleonora Misino, Giuseppe Marra and Emanuele Sansone. VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming