Jacob Andreas, Marcus Rohrbach, Trevor Darrell & Dan Klein (2016):
Learning to Compose Neural Networks for Question Answering.
In: Proceedings of the 2016 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,
pp. 1545–1554,
doi:10.18653/v1/n16-1181.
Franz Baader, Diego Calvanese, Deborah L. McGuinness, Daniele Nardi & Peter F. Patel-Schneider (2003):
The Description Logic Handbook: Theory, Implementation, and Applications.
Cambridge University Press.
Gerhard Brewka, Ilkka Niemelä & Miroslaw Truszczynski (2011):
Answer Set Programming at a Glance.
Communications of the ACM 54(12),
pp. 92–103,
doi:10.1145/2043174.2043195.
Francesco Calimeri, Wolfgang Faber, Martin Gebser, Giovambattista Ianni, Roland Kaminski, Thomas Krennwallner, Nicola Leone, Marco Maratea, Francesco Ricca & Torsten Schaub (2020):
ASP-Core-2 input language format.
Theory and Practice of Logic Programming 20(2),
pp. 294–309,
doi:10.1017/S1471068419000450.
William W Cohen, Fan Yang & Kathryn Rivard Mazaitis (2018):
TensorLog: Deep Learning Meets Probabilistic Databases.
Journal of Artificial Intelligence Research 1,
pp. 1–15.
Ivan Donadello, Luciano Serafini & Artur D'Avila Garcez (2017):
Logic tensor networks for semantic image interpretation.
In: Proceedings of the 26th International Joint Conference on Artificial Intelligence.
AAAI Press,
pp. 1596–1602,
doi:10.24963/ijcai.2017/221.
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick & Ross Girshick (2017):
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,
pp. 2901–2910,
doi:10.1109/CVPR.2017.215.
Seyed Mehran Kazemi & David Poole (2018):
RelNN: A deep neural model for relational learning.
In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence.
Thomas N. Kipf & Max Welling (2017):
Semi-Supervised Classification with Graph Convolutional Networks.
In: Proceedings of the 5th International Conference on Learning Representations, ICLR 2017.
Doga Kisa, Guy Van den Broeck, Arthur Choi & Adnan Darwiche (2014):
Probabilistic sentential decision diagrams.
In: Fourteenth International Conference on the Principles of Knowledge Representation and Reasoning.
Joohyung Lee & Yi Wang (2016):
Weighted Rules under the Stable Model Semantics.
In: Proceedings of International Conference on Principles of Knowledge Representation and Reasoning (KR),
pp. 145–154.
Joohyung Lee & Yi Wang (2018):
Weight Learning in a Probabilistic Extension of Answer Set Programs.
In: Proceedings of International Conference on Principles of Knowledge Representation and Reasoning (KR),
pp. 22–31.
Joohyung Lee & Zhun Yang (2017):
LPMLN, Weak Constraints, and P-log.
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI),
pp. 1170–1177.
Yuliya Lierler & Marco Maratea (2004):
Cmodels-2: SAT-based answer set solver enhanced to non-tight programs.
In: Proceedings of International Conference on Logic Programming and NonMonotonic Reasoning.
Springer,
pp. 346–350,
doi:10.1007/978-3-540-24609-1_32.
Vladimir Lifschitz (2008):
What Is Answer Set Programming?.
In: Proceedings of the AAAI Conference on Artificial Intelligence.
MIT Press,
pp. 1594–1597.
Bill Yuchen Lin, Xinyue Chen, Jamin Chen & Xiang Ren (2019):
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning.
In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP),
pp. 2822–2832,
doi:10.18653/v1/D19-1282.
Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester & Luc De Raedt (2018):
Deepproblog: Neural probabilistic logic programming.
In: Proceedings of Advances in Neural Information Processing Systems,
pp. 3749–3759.
Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum & Jiajun Wu (2019):
The neuro-symbolic concept learner: interpreting scenes, words, and sentences from natural supervision.
In: Proceedings of International Conference on Learning Representations.
Rasmus Palm, Ulrich Paquet & Ole Winther (2018):
Recurrent relational networks.
In: Proceedings of Advances in Neural Information Processing Systems,
pp. 3368–3378.
Judea Pearl (2000):
Causality: models, reasoning and inference 29.
Cambridge Univ Press.
Raymond Reiter (1980):
A logic for default reasoning.
Artificial Intelligence 13,
pp. 81–132,
doi:10.1016/0004-3702(80)90014-4.
Tim Rocktäschel & Sebastian Riedel (2017):
End-to-end differentiable proving.
In: Proceedings of Advances in Neural Information Processing Systems,
pp. 3788–3800.
Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura & David L. Dill (2019):
Learning a SAT Solver from Single-Bit Supervision.
In: Proceedings of the 7th International Conference on Learning Representations (ICLR).
Gustav Šourek, Vojtech Aschenbrenner, Filip Železny & Ondřej Kuželka (2015):
Lifted relational neural networks.
In: Proceedings of the 2015th International Conference on Cognitive Computation: Integrating Neural and Symbolic Approaches-Volume 1583.
CEUR-WS. org,
pp. 52–60.
Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai & Yoav Artzi (2019):
A Corpus for Reasoning about Natural Language Grounded in Photographs.
In: Proceedings of the 57th Conference of the Association for Computational Linguistics (ACL),
pp. 6418–6428,
doi:10.18653/v1/p19-1644.
Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang & Guy Van den Broeck (2018):
A Semantic Loss Function for Deep Learning with Symbolic Knowledge.
In: Proceedings of the 35th International Conference on Machine Learning (ICML).
Available at http://starai.cs.ucla.edu/papers/XuICML18.pdf.
Zhun Yang, Adam Ishay & Joohyung Lee (2020):
NeurASP: Embracing Neural Networks into Answer Set Programming.
In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI),
pp. 1755–1762,
doi:10.1017/S1471068419000450.
Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba & Joshua B. Tenenbaum (2020):
CLEVRER: Collision Events for Video Representation and Reasoning.
In: Proceedings of the 8th International Conference on Learning Representations (ICLR).
eptcs@eptcs.org