Table of Contents

• A Roadmap for Neuro-argumentative LearningMaurizio Proietti, Francesca Toni. 1-8 [doi]
• What's Wrong with Gradient-based Complex Query Answering?Ouns El Harzli, Samy Badreddine, Tarek R. Besold. 9-18 [doi]
• Closing the Neural-Symbolic Cycle: Knowledge Extraction, User Intervention and Distillation from Convolutional Neural NetworksKwun Ho Ngan, James Phelan, Esma Mansouri-Benssassi, Joe Townsend, Artur S. d'Avila Garcez. 19-43 [doi]
• The Challenge of Learning Symbolic RepresentationsLuca Salvatore Lorello, Marco Lippi 0001. 44-61 [doi]
• Exploring Mathematical Conjecturing with Large Language ModelsMoa Johansson, Nicholas Smallbone. 62-77 [doi]
• Learning Logic Constraints From DemonstrationMattijs Baert, Sam Leroux, Pieter Simoens. 78-84 [doi]
• From Axioms over Graphs to Vectors, and Back Again: Evaluating the Properties of Graph-based Ontology EmbeddingsFernando Zhapa-Camacho, Robert Hoehndorf. 85-102 [doi]
• Neural-Symbolic Predicate Invention: Learning Relational Concepts from Visual ScenesJingyuan Sha, Hikaru Shindo, Kristian Kersting, Devendra Singh Dhami. 103-117 [doi]
• Semantic Interpretability of Convolutional Neural Networks by Taxonomy ExtractionVitor A. C. Horta, Robin Sobczyk, Maarten C. Stol, Alessandra Mileo. 118-127 [doi]
• Preliminary Results on a State-Driven Method for Rule Construction in Neural-Symbolic Reinforcement LearningDavide Beretta, Stefania Monica, Federico Bergenti. 128-138 [doi]
• A Modular Neurosymbolic Approach for Visual Graph Question AnsweringThomas Eiter, Nelson Higuera Ruiz, Johannes Oetsch. 139-149 [doi]
• Is the Proof Length a Good Indicator of Hardness for Reason-able Embeddings?Jedrzej Potoniec. 150-161 [doi]
• Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their LimitationsEmanuele Marconato, Stefano Teso, Andrea Passerini. 162-166 [doi]
• Knowledge-Guided Colorization: Overview, Prospects and ChallengesRory Ward, Muhammad Jaleed Khan, John G. Breslin, Edward Curry. 167-173 [doi]
• Towards Invertible Semantic-Preserving Embeddings of Logical FormulaeGaia Saveri, Luca Bortolussi. 174-194 [doi]
• Implementing Trustworthy AI in Real-world Medical Imaging using the SimpleMind Software EnvironmentMatthew S. Brown, M. Wasil Wahi-Anwar, Youngwon Choi, Morgan Daly, Liza Shrestha, Koon-Pong Wong, Jonathan G. Goldin, Dieter R. Enzmann. 195-203 [doi]
• Large Language Models Need Symbolic AIKristian J. Hammond, David B. Leake. 204-209 [doi]
• Continual Reasoning: Non-monotonic Reasoning in Neurosymbolic AI using Continual LearningSofoklis Kyriakopoulos, Artur S. d'Avila Garcez. 210-222 [doi]
• Designing Logic Tensor Networks for Visual Sudoku Puzzle ClassificationLia 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 Sebastian Russo, Marco Russo, Francesco Taioli, Lorenzo Vaiani, Chiara Vercellino. 223-232 [doi]
• Inductive Future Time Prediction on Temporal Knowledge Graphs with Interval TimeRoxana Pop, Egor V. Kostylev. 233-240 [doi]
• Challenge Problems in Developing a Neuro-Symbolic OODA LoopAlberto Speranzon, Christian H. Debrunner, David Rosenbluth, Mauricio Castillo-Effen, Anthony R. Nowicki, Kevin Alcedo, Andrzej Banaszuk. 241-247 [doi]
• How to Think About Benchmarking Neurosymbolic AI?Johanna Ott, Arthur Ledaguenel, Céline Hudelot, Mattis Hartwig. 248-254 [doi]
• Visual Reward MachinesElena Umili, Francesco Argenziano, Aymeric Barbin, Roberto Capobianco. 255-267 [doi]
• PhysWM: Physical World Models for Robot LearningMarc Otto, Octavio Arriaga, Chandandeep Singh, Jichen Guo, Frank Kirchner. 268-278 [doi]
• Decoding Superpositions of Bound Symbols Represented by Distributed RepresentationsMichael Hersche, Zuzanna Opala, Geethan Karunaratne, Abu Sebastian, Abbas Rahimi. 279-288 [doi]
• A Hybrid System for Systematic Generalization in Simple Arithmetic ProblemsFlavio Petruzzellis, Alberto Testolin, Alessandro Sperduti. 289-301 [doi]
• VSA-based Positional Encoding Can Replace Recurrent Networks in Emergent Symbol BindingFrancesco S. Carzaniga, Michael Hersche, Kaspar Schindler, Abbas Rahimi. 302-326 [doi]
• On the Benefits of OWL-based Knowledge Graphs for Neural-Symbolic SystemsDavid Herron, Ernesto Jiménez-Ruiz, Tillman Weyde. 327-335 [doi]
• Causality Prediction with Neural-Symbolic Systems: A Case Study in Smart GridsKatrin Schreiberhuber, Marta Sabou, Fajar J. Ekaputra, Peter Knees, Peb Ruswono Aryan, Alfred Einfalt, Ralf Mosshammer. 336-347 [doi]
• Towards Explainable Decision Making with Neural Program Synthesis and Library LearningManuel Eberhardinger, Johannes Maucher, Setareh Maghsudi. 348-368 [doi]
• Exploiting T-norms for Deep Learning in Autonomous DrivingMihaela C. Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz. 369-380 [doi]
• FB15k-CVT: A Challenging Dataset for Knowledge Graph Embedding ModelsMouloud Iferroudjene, Victor Charpenay, Antoine Zimmermann. 381-394 [doi]
• Explainable Classification of Internet MemesAbhinav Kumar Thakur, Filip Ilievski, Hông-Ân Sandlin, Zhivar Sourati, Luca Luceri, Riccardo Tommasini 0001, Alain Mermoud. 395-409 [doi]
• GlanceNets: Interpretable, Leak-proof Concept-based ModelsEmanuele Marconato, Andrea Passerini, Stefano Teso. 410 [doi]
• Learning Where and When to Reason in Neuro-Symbolic InferenceCristina Cornelio, Jan Stühmer, Shell Xu Hu, Timothy M. Hospedales. 411-412 [doi]
• Semantic Probabilistic Layers for Neuro-Symbolic LearningKareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari. 413 [doi]
• RL-Net: Interpretable Rule Learning with Neural NetworksLucile Dierckx, Rosana Veroneze, Siegfried Nijssen. 414-415 [doi]
• Generalizable Neuro-Symbolic Systems for Commonsense Question AnsweringAlessandro Oltramari, Jonathan Francis, Filip Ilievski, Kaixin Ma, Roshanak Mirzaee. 416-417 [doi]
• Deep Symbolic Learning: Discovering Symbols and Rules from PerceptionsAlessandro Daniele, Tommaso Campari, Sagar Malhotra, Luciano Serafini. 418-419 [doi]
• The Roles of Symbols in Neural-based AI: They are Not What You Think!Daniel L. Silver, Tom M. Mitchell. 420-421 [doi]
• Interpretable Neural-Symbolic Concept ReasoningPietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Liò, Frédéric Precioso, Mateja Jamnik, Giuseppe Marra. 422-423 [doi]
• Combining Machine Learning and Semantic Web: A Systematic Mapping StudyAnna Breit, Laura Waltersdorfer, Fajar J. Ekaputra, Marta Sabou, Andreas Ekelhart, Andreea Iana, Heiko Paulheim, Jan Portisch, Artem Revenko, Frank van Harmelen, Annette ten Teije. 424 [doi]
• Solving Raven's Progressive Matrices via a Neuro-vector-symbolic ArchitectureMichael Hersche, Mustafa Zeqiri, Luca Benini, Abu Sebastian, Abbas Rahimi. 425-426 [doi]
• Verifying Strategic Abilities of Neural-Symbolic Multi-agent SystemsMichael Akintunde, Elena Botoeva, Panagiotis Kouvaros, Alessio Lomuscio. 427 [doi]
• Safe Reinforcement Learning via Probabilistic Logic ShieldsWen-Chi Yang, Giuseppe Marra, Gavin Rens, Luc De Raedt. 428-429 [doi]
• Neural Class Expression SynthesisN'Dah Jean Kouagou, Stefan Heindorf, Caglar Demir, Axel-Cyrille Ngonga Ngomo. 430-431 [doi]
• Logic Explained NetworksGabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Marco Gori, Pietro Liò, Marco Maggini, Stefano Melacci. 432-433 [doi]
• VAEL: Bridging Variational Autoencoders and Probabilistic Logic ProgrammingEleonora Misino, Giuseppe Marra, Emanuele Sansone. 434-435 [doi]