Elamur Research⌕
☰
home / papers / 2021 / Neuro-Symbolic Artificial Intelligence: Current Trends

Neuro-Symbolic Artificial Intelligence: Current Trends

Paper · 2021 · Md Kamruzzaman Sarker, Lu Zhou, Aaron Eberhart et al.

Summary

This survey maps recent work in neuro-symbolic AI (NeSy AI), the area that combines trainable neural networks with explicit symbolic methods such as formal logic. Its goal is to give researchers an organized entry point into a field that is growing fast but is spread across many venues. The authors screened original research papers on the topic published since 2011 at five major conferences (ICML, NeurIPS, AAAI, IJCAI and ICLR), mostly judging relevance from abstracts. They sorted these papers with two schemes: the five architectural patterns Henry Kautz described in his AAAI 2020 lecture, and the eight-dimension taxonomy from a 2005 survey. They also checked which expected benefits of NeSy systems each paper actually pursues. Recent work uses richer logics than two decades ago, but much of it still stays at a non-logical symbolic level. Interpretability dominates, while other promised advantages are rarely addressed. The authors argue that the field should make deeper and more systematic use of formal logic, and they suggest the Semantic Web and deep deductive reasoners as useful testbeds.

Key points

  • Literature corpus: Papers came from ICML, NeurIPS, AAAI, IJCAI and ICLR, from 2011 onward (ICLR from 2013). Surveys and overviews were excluded.
  • Kautz's five categories: These describe how neural and symbolic components are wired together:
    • [symbolic Neuro symbolic]
    • [Symbolic[Neuro]], for example AlphaGo
    • [Neuro ∪ compile(Symbolic)]
    • [Neuro → Symbolic], for example the Neuro-Symbolic Concept-Learner
    • [Neuro[Symbolic]]
  • The 2005 taxonomy: It has eight dimensions grouped under three aspects:
    • Interrelation: integrated/hybrid, neuronal/connectionist, local/distributed, standard/nonstandard
    • Language: symbolic/logical, propositional/first-order
    • Usage: extraction/representation, learning/reasoning
  • Targeted benefits: Interpretability is by far the most common goal. Few papers deal with out-of-distribution data or learning from small data, and error recovery is almost never addressed.
  • Outlook: The authors recommend experiments with graded logical complexity, moving from flat tags to class hierarchies such as schema.org and on to richer Semantic Web logics. Deep deductive reasoners, tested on logics from RDF up to first-order logic, show that deduction remains hard for deep learning.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Kansas State UniversityManhattan · United States4 works · 190 citations

Also on the byline: University of Hartford.

 Fields, topics and concepts

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Artificial intelligence · 49 works

Cite

Cite as Md Kamruzzaman Sarker, Lu Zhou, Aaron Eberhart and Pascal Hitzler. 2021. Neuro-Symbolic Artificial Intelligence: Current Trends. arXiv:2105.05330.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2105.05330 · arXiv 2105.05330

@misc{sarker2021neuro,
  title = {{Neuro-Symbolic Artificial Intelligence: Current Trends}},
  author = {Md Kamruzzaman Sarker, Lu Zhou, Aaron Eberhart and Pascal Hitzler},
  year = {2021},
  doi = {10.48550/arxiv.2105.05330},
  eprint = {2105.05330},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2105.05330}
}

References

61 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. P. Hitzler and A.K. Seda, Generalized metrics and uniquely determined logic programs, Theor. Comput. Sci. 305(1–3) (2003), 187–219. doi:10.1016/S0304-3975(02)00709-0. — [doi:10.1016/s0304-3975(02)00709-0](https://doi.org/10.1016/s0304-3975%2802%2900709-0) find
  2. W.S. McCulloch and W. Pitts, A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics 5 (1943), 115–133. find
  3. T.R. Besold, A.S. d’Avila Garcez, S. Bader, H. Bowman, P.M. Domingos, P. Hitzler, K. Kühnberger, L.C. Lamb, D. Lowd, P.M.V. Lima, L. de Penning, G. Pinkas, H. Poon and G. Zaverucha, Neural-Symbolic Learning and Reasoning: A Survey and Interpretation, CoRR abs/1711.03902 (2017). http://arxiv.org/abs/… ↗
  4. S. Bader and P. Hitzler, Dimensions of Neural-symbolic Integration – A Structured Survey, in: We Will Show Them! Essays in Honour of Dov Gabbay, Volume One, S.N. Artëmov, H. Barringer, A.S. d’Avila Garcez, L.C. Lamb and J. Woods, eds, College Publications, 2005, pp. 167–194. find
  5. B. Hammer and P. Hitzler (eds), Perspectives of Neural-Symbolic Integration, Studies in Computational Intelligence, Vol. 77, Springer, 2007. ISBN 978-3-540-73953-1. find
  6. A.S. d’Avila Garcez, L.C. Lamb and D.M. Gabbay, Neural-Symbolic Cognitive Reasoning, Cognitive Technologies, Springer, 2009. ISBN 978-3-540-73245-7. doi:10.1007/978-3-540-73246-4. — [doi:10.1007/978-3-540-73246-4](https://doi.org/10.1007/978-3-540-73246-4) find
  7. G. Lample and F. Charton, Deep Learning For Symbolic Mathematics, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. https://openreview.net/forum?id=S1eZYeHFDS. ↗
  8. J. Mao, C. Gan, P. Kohli, J.B. Tenenbaum and J. Wu, The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision, in: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, OpenReview.net, 2019. https://open… find
  9. F. Yang, D. Lyu, B. Liu and S. Gustafson, PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making, in: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, J. Lang… — [doi:10.24963/ijcai.2018/675](https://doi.org/10.24963/ijcai.2018/675) find
  10. D. Demeter and D. Downey, Just Add Functions: A Neural-Symbolic Language Model, in: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in… find
  11. D. Lyu, F. Yang, B. Liu and S. Gustafson, SDRL: Interpretable and Data-Efficient Deep Reinforcement Learning Leveraging Symbolic Planning, in: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI… — [doi:10.1609/aaai.v33i01.33012970](https://doi.org/10.1609/aaai.v33i01.33012970) find
  12. S. Cao, W. Lu and Q. Xu, Deep Neural Networks for Learning Graph Representations, in: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, D. Schuurmans and M.P. Wellman, eds, AAAI Press, 2016, pp. 1145–1152. http://www.aaai.org/ocs/in… ↗
  13. X. Chen, C. Liang, A.W. Yu, D. Song and D. Zhou, Compositional Generalization via Neural-Symbolic Stack Machines, in: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, H. Larochelle, M. R… find
  14. Y. Xie, Z. Xu, K.S. Meel, M.S. Kankanhalli and H. Soh, Embedding Symbolic Knowledge into Deep Networks, in: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, H.M. Wallach, H… find
  15. R. Manhaeve, S. Dumancic, A. Kimmig, T. Demeester and L.D. Raedt, DeepProbLog: Neural Probabilistic Logic Programming, in: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, S. Ben…
  16. F. Arabshahi, S. Singh and A. Anandkumar, Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs, in: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, OpenReview.net, 2018.… find
  17. L. de Penning, A.S. d’Avila Garcez, L.C. Lamb and J.C. Meyer, A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning, in: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, T. Walsh, ed., IJCAI/AA… — [doi:10.5591/978-1-57735-516-8/ijcai11-278](https://doi.org/10.5591/978-1-57735-516-8/ijcai11-278) find
  18. J. Jiang and S. Ahn, Generative Neurosymbolic Machines, in: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan and H. Lin, eds, 2020. https… find
  19. X. Liang, Z. Hu, H. Zhang, L. Lin and E.P. Xing, Symbolic Graph Reasoning Meets Convolutions, in: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, S. Bengio, H.M. Wallach, H. Lar… find
  20. W.W. Cohen, H. Sun, R.A. Hofer and M. Siegler, Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. https://openreview.net/forum?id=BJlguT4YPr. ↗
  21. K.K. Teru, E. Denis and W. Hamilton, Inductive Relation Prediction by Subgraph Reasoning, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, Proceedings of Machine Learning Research, Vol. 119, PMLR, 2020, pp. 9448–9457. http://proceed… find
  22. S. Garg, A. Bajpai and Mausam, Symbolic Network: Generalized Neural Policies for Relational MDPs, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, Proceedings of Machine Learning Research, Vol. 119, PMLR, 2020, pp. 3397–3407. http:/… find
  23. L. Mou, Z. Lu, H. Li and Z. Jin, Coupling Distributed and Symbolic Execution for Natural Language Queries, in: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, D. Precup and Y.W. Teh, eds, Proceedings of Machine Learning Resea… find
  24. C. Xiao, M. Dymetman and C. Gardent, Symbolic Priors for RNN-based Semantic Parsing, in: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, C. Sierra, ed., ijcai.org, 2017, pp. 4186–4192. doi:10.24963/ijcai… — [doi:10.24963/ijcai.2017/585](https://doi.org/10.24963/ijcai.2017/585) find
  25. R. Dang-Nhu, PLANS: Neuro-Symbolic Program Learning from Videos, in: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan and H. Lin, eds, 20… find
  26. K. Yi, J. Wu, C. Gan, A. Torralba, P. Kohli and J. Tenenbaum, Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding, in: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 20… find
  27. X. Chen, C. Liang, A.W. Yu, D. Zhou, D. Song and Q.V. Le, Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.… find
  28. E. Parisotto, A. Mohamed, R. Singh, L. Li, D. Zhou and P. Kohli, Neuro-Symbolic Program Synthesis, in: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net, 2017. https://openreview.net/forum?id=rJ0JwFcex… ↗
  29. P. Hohenecker and T. Lukasiewicz, Ontology Reasoning with Deep Neural Networks, J. Artif. Intell. Res. 68 (2020), 503–540. doi:10.1613/jair.1.11661. — [doi:10.1613/jair.1.11661](https://doi.org/10.1613/jair.1.11661) find
  30. J. Xu, Z. Zhang, T. Friedman, Y. Liang and G.V. den Broeck, A Semantic Loss Function for Deep Learning with Symbolic Knowledge, in: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, J.G. Dy and A. Krause, eds, Proc… find
  31. A. Santoro, F. Hill, D.G.T. Barrett, A.S. Morcos and T.P. Lillicrap, Measuring abstract reasoning in neural networks, in: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, J.G. Dy and A. Krause, eds, Proceedings of… find
  32. R. Trivedi, H. Dai, Y. Wang and L. Song, Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs, in: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, D. Precup and Y.W. Teh, eds, Proceedings of Machine Learning Rese… find
  33. M. Allamanis, P. Chanthirasegaran, P. Kohli and C. Sutton, Learning Continuous Semantic Representations of Symbolic Expressions, in: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, D. Precup and Y.W. Teh, eds, Proceedings of… find
  34. M. Asai and C. Muise, Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS), in: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, C. Bessiere, ed., ijcai.org, 2020, pp. 2676–2682. doi:10.24963/ijc… — [doi:10.24963/ijcai.2020/371](https://doi.org/10.24963/ijcai.2020/371) find
  35. I. Donadello, L. Serafini and A.S. d’Avila Garcez, Logic Tensor Networks for Semantic Image Interpretation, in: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, C. Sierra, ed., ijcai.org, 2017, pp. 1596–1… find
  36. M. Asai and A. Fukunaga, Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary, in: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium… find
  37. M.D. Cranmer, A. Sanchez-Gonzalez, P.W. Battaglia, R. Xu, K. Cranmer, D.N. Spergel and S. Ho, Discovering Symbolic Models from Deep Learning with Inductive Biases, in: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020,… find
  38. A.M. Alaa and M. van der Schaar, Demystifying Black-box Models with Symbolic Metamodels, in: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, H.M. Wallach, H. Larochelle, A… find
  39. X. Zhang, A. Solar-Lezama and R. Singh, Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections, in: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada,… find
  40. B. Dhingra, M. Zaheer, V. Balachandran, G. Neubig, R. Salakhutdinov and W.W. Cohen, Differentiable Reasoning over a Virtual Knowledge Base, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. https://openreview.net/… ↗
  41. S. Amizadeh, H. Palangi, A. Polozov, Y. Huang and K. Koishida, Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning", in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, Proceedings of Machine Learning Research, Vol.… find
  42. Q. Li, S. Huang, Y. Hong, Y. Chen, Y.N. Wu and S. Zhu, Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, Proceedings of… find
  43. D. Chen, Y. Bai, W. Zhao, S. Ament, J.M. Gregoire and C.P. Gomes, Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, Proceedings of Machine Learning… find
  44. P. Minervini, S. Riedel, P. Stenetorp, E. Grefenstette and T. Rocktäschel, Learning Reasoning Strategies in End-to-End Differentiable Proving, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, Proceedings of Machine Learning Research… find
  45. R. Vedantam, K. Desai, S. Lee, M. Rohrbach, D. Batra and D. Parikh, Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering, in: Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, K. Chaudhuri and R.… find
  46. H. Young, O. Bastani and M. Naik, Learning Neurosymbolic Generative Models via Program Synthesis, in: Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, K. Chaudhuri and R. Salakhutdinov, eds, Proceedings of Machine Learning… find
  47. P. Wang, P.L. Donti, B. Wilder and J.Z. Kolter, SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver, in: Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, K. Chaudhuri and R. Sal… find
  48. M. Fischer, M. Balunovic, D. Drachsler-Cohen, T. Gehr, C. Zhang and M.T. Vechev, DL2: Training and Querying Neural Networks with Logic, in: Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, K. Chaudhuri and R. Salakhutdinov,… find
  49. Z. Jiang and S. Luo, Neural Logic Reinforcement Learning, in: Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, K. Chaudhuri and R. Salakhutdinov, eds, Proceedings of Machine Learning Research, Vol. 97, PMLR, 2019, pp. 3110–… find
  50. C. Xiong, S. Merity and R. Socher, Dynamic Memory Networks for Visual and Textual Question Answering, in: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, M. Balcan and K.Q. Weinberger, eds, JMLR Workshop and Conference Procee… find
  51. L. Shastri, Advances in SHRUTI-A Neurally Motivated Model of Relational Knowledge Representation and Rapid Inference Using Temporal Synchrony, Appl. Intell. 11(1) (1999), 79–108. doi:10.1023/A:1008380614985. — [doi:10.1023/a:1008380614985](https://doi.org/10.1023/a:1008380614985) find
  52. J. McCarthy, Epistomological challenges for connectionism, Behavioral and Brain Sciences 11 (1988), 44. find
  53. F. van Harmelen and A. ten Teije, A Boxology of Design Patterns forHybrid Learningand Reasoning Systems, J. Web Eng. 18(1–3) (2019), 97–124. doi:10.13052/jwe1540-9589.18133. — [doi:10.13052/jwe1540-9589.18133](https://doi.org/10.13052/jwe1540-9589.18133) find
  54. P. Hitzler, A review of the semantic web field, Commun. ACM 64(2) (2021), 76–83. doi:10.1145/3397512. — [doi:10.1145/3397512](https://doi.org/10.1145/3397512) find
  55. P. Hitzler, M. Krötzsch and S. Rudolph, Foundations of Semantic Web Technologies, Chapman and Hall/CRC Press, 2010. ISBN 9781420090505. http://www.semantic-web-book.org/. ↗
  56. P. Hitzler, F. Bianchi, M. Ebrahimi and M.K. Sarker, Neural-symbolic integration and the Semantic Web, Semantic Web 11(1) (2020), 3–11. doi:10.3233/SW-190368. — [doi:10.3233/sw-190368](https://doi.org/10.3233/sw-190368) find
  57. M. Ebrahimi, A. Eberhart, F. Bianchi and P. Hitzler, Towards Bridging the Neuro-Symbolic Gap: Deep Deductive Reasoners, Applied Intelligence (2021), to appear. find
  58. B. Makni and J.A. Hendler, Deep learning for noise-tolerant RDFS reasoning, Semantic Web 10(5) (2019), 823–862. doi:10.3233/SW-190363. — [doi:10.3233/sw-190363](https://doi.org/10.3233/sw-190363) find
  59. F. Bianchi and P. Hitzler, On the Capabilities of Logic Tensor Networks for Deductive Reasoning, in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019) Stanford University, Palo Alto, California, USA, March 25-27, 2019., Stanford U… find
  60. M. Ebrahimi, M.K. Sarker, F. Bianchi, N. Xie, A. Eberhart, D. Doran, H. Kim and P. Hitzler, Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment, in: Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021), Stanford Un… find
  61. A. Eberhart, M. Ebrahimi, L. Zhou, C. Shimizu and P. Hitzler, Completion Reasoning Emulation for the Description Logic EL+, in: Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice, AAAI-MAKE 2020, Palo Alto, CA, USA, March 23-25, 2020, Vo… find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/sarker-2021-neuro-symbolic-artificial-intelligence/