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Neuro Symbolic Applications

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Neuro Symbolic Reasoning and Learning

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

Although these days neural networks and deep learning get equated with AI, there are many systems that combine neural reasoning and learning with symbolic reasoning and learning modules. In the previous chapters, we discussed some of these approaches with more focus on formalisms. In this chapter we point to some of the notable applications, elaborate on some of the applications in visual question answering, and natural language processing domains, and touch upon neurosymbolic reinforcement learning.

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References

  1. Aggarwal, G., Parikh, D.: Neuro-symbolic generative art: a preliminary study. Preprint (2020). arXiv:2007.02171

    Google Scholar 

  2. Alshiekh, M., Bloem, R., Ehlers, R., Könighofer, B., Niekum, S., Topcu, U.: Safe reinforcement learning via shielding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  3. Amizadeh, S., Palangi, H., Polozov, A., Huang, Y., Koishida, K.: Neuro-symbolic visual reasoning: Disentangling visual from reasoning. In: International Conference on Machine Learning, pp. 279–290. PMLR (2020)

    Google Scholar 

  4. Anderson, G., Verma, A., Dillig, I., Chaudhuri, S.: Neurosymbolic reinforcement learning with formally verified exploration. Adv. Neural Inf. Process. Syst. 33, 6172–6183 (2020)

    Google Scholar 

  5. Andreas, J., Rohrbach, M., Darrell, T., Klein, D.: Neural module networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 39–48. IEEE Computer Society (2016)

    Google Scholar 

  6. Baral, C., Dzifcak, J.: Solving puzzles described in English by automated translation to answer set programming and learning how to do that translation. In: 2011 AAAI Fall Symposium Series (2011)

    Google Scholar 

  7. Baral, C., Eiter, T., Bjäreland, M., Nakamura, M.: Maintenance goals of agents in a dynamic environment: formulation and policy construction. Artif. Intell. 172(12–13), 1429–1469 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  8. Baral, C., Zhao, J.: Goal specification, non-determinism and quantifying over policies. In: Proceedings of the 21st national conference on Artificial intelligence, vol. 1, pp. 231–237 (2006)

    Google Scholar 

  9. Bosselut, A., Le Bras, R., Choi, Y.: Dynamic neuro-symbolic knowledge graph construction for zero-shot commonsense question answering. In: AAAI, pp. 4923–4931 (2021)

    Google Scholar 

  10. Bouneffouf, D., Aggarwal, C.C.: Survey on applications of neurosymbolic artificial intelligence. Preprint (2022). arXiv:2209.12618

    Google Scholar 

  11. Camacho, A., Icarte, R.T., Klassen, T.Q., McIlraith, S.A.: Ltl and beyond: formal languages for reward function specification in reinforcement learning. In: IJCAI (2019)

    Google Scholar 

  12. Cambria, E., Liu, Q., Decherchi, S., Xing, F., Kwok, K.: Senticnet 7: a commonsense-based neurosymbolic ai framework for explainable sentiment analysis. In: Proceedings of LREC 2022 (2022)

    Google Scholar 

  13. Chen, W., Ma, X., Wang, X., Cohen, W.W.: Program of thoughts prompting: disentangling computation from reasoning for numerical reasoning tasks. Preprint (2022). arXiv:2211.12588

    Google Scholar 

  14. Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yáñez, J.C.: Neuro-symbolic system for business internal control. In: Industrial Conference on Data Mining, pp. 1–10. Springer (2004)

    Google Scholar 

  15. Drancé, M.: Neuro-symbolic xai: application to drug repurposing for rare diseases. In: International Conference on Database Systems for Advanced Applications, pp. 539–543. Springer (2022)

    Google Scholar 

  16. Eiter, T., Higuera, N., Oetsch, J., Pritz, M.: A neuro-symbolic ASP pipeline for visual question answering. Preprint (2022). arXiv:2205.07548

    Google Scholar 

  17. Gupta, N., Lin, K., Roth, D., Singh, S., Gardner, M.: Neural module networks for reasoning over text. In: International Conference on Learning Representations (2019)

    Google Scholar 

  18. Hanson, D., Imran, A., Vellanki, A., Kanagaraj, S.: A neuro-symbolic humanlike arm controller for Sophia the robot. Preprint (2020). arXiv:2010.13983

    Google Scholar 

  19. Hatzilygeroudis, I., Prentzas, J.: Fuzzy and neuro-symbolic approaches to assessment of bank loan applicants. In: Artificial Intelligence Applications and Innovations, pp. 82–91. Springer (2011)

    Google Scholar 

  20. Jabrayilzade, E., Tekir, S.: LGPSolver – solving logic grid puzzles automatically. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 1118–1123 (2020)

    Article  Google Scholar 

  21. Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C.L., Girshick, R.B.: Clevr: a diagnostic dataset for compositional language and elementary visual reasoning. In: CVPR (2017)

    Google Scholar 

  22. Kalithasan, N., Singh, H., Bindal, V., Tuli, A., Agrawal, V., Jain, R., Singla, P., Paul, R.: Learning neuro-symbolic programs for language guided robot manipulation. Preprint (2022). arXiv:2211.06652

    Google Scholar 

  23. Kapanipathi, P., Abdelaziz, I., Ravishankar, S., Roukos, S., Gray, A., Astudillo, R., Chang, M., Cornelio, C., Dana, S., Fokoue, A., et al.: Question answering over knowledge bases by leveraging semantic parsing and neuro-symbolic reasoning. Preprint (2020). arXiv:2012.01707

    Google Scholar 

  24. Karth, I., Aytemiz, B., Mawhorter, R., Smith, A.M.: Neurosymbolic map generation with VQ-VAE and WFC. In: The 16th International Conference on the Foundations of Digital Games (FDG) 2021, pp. 1–6 (2021)

    Google Scholar 

  25. Lavin, A.: Neuro-symbolic neurodegenerative disease modeling as probabilistic programmed deep kernels. In: International Workshop on Health Intelligence, pp. 49–64. Springer (2021)

    Google Scholar 

  26. Lee, J.H., Sioutis, M., Ahrens, K., Alirezaie, M., Kerzel, M., Wermter, S.: Neuro-symbolic spatio-temporal reasoning. Preprint (2022). arXiv:2211.15566

    Google Scholar 

  27. Lev, I., MacCartney, B., Manning, C.D., Levy, R.: Solving logic puzzles: from robust processing to precise semantics. In: Proceedings of the 2nd Workshop on Text Meaning and Interpretation, pp. 9–16 (2004)

    Google Scholar 

  28. Liao, H.C.: A survey of reinforcement learning with temporal logic rewards. TUM (2020). https://mediatum.ub.tum.de/doc/1579215/1579215.pdf

  29. Mao, J., Gan, C., Kohli, P., Tenenbaum, J.B., Wu, J.: The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. In: International Conference on Learning Representations. International Conference on Learning Representations, ICLR (2019)

    Google Scholar 

  30. Marques, N.C., Bader, S., Rocio, V., Hölldobler, S.: Neuro-symbolic word tagging. In: International Review on Computers and Software (2021)

    Google Scholar 

  31. Martin, L.J.: Neurosymbolic automated story generation. Ph.D. Thesis, Georgia Institute of Technology, 2021

    Google Scholar 

  32. Mei, L., Mao, J., Wang, Z., Gan, C., Tenenbaum, J.: Falcon: fast visual concept learning by integrating images, linguistic descriptions, and conceptual relations. In: International Conference on Learning Representations (2022)

    Google Scholar 

  33. Milicevic, A., Near, J.P., Singh, R.: Puzzler: An automated logic puzzle solver (2012). http://people.csail.mit.edu/jnear/puzzler/writeup.html

  34. Mitchener, L., Tuckey, D., Crosby, M., Russo, A.: Detect, understand, act: a neuro-symbolic hierarchical reinforcement learning framework. Mach. Learn. 111(4), 1523–1549 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  35. Mitra, A., Baral, C.: Learning to automatically solve logic grid puzzles. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1023–1033 (2015)

    Google Scholar 

  36. Mitra, A., Clark, P., Tafjord, O., Baral, C.: Declarative question answering over knowledge bases containing natural language text with answer set programming. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3003–3010 (2019)

    Google Scholar 

  37. Morel, G.: Neuro-symbolic AI for the smart city. In: Journal of Physics: Conference Series, vol. 2042. IOP Publishing (2021)

    Google Scholar 

  38. Palangi, H., Bosselut, A., Dasigi, P.: Neuro-symbolic methods for language and vision. In: AAAI 2022 Tutorial (2022)

    Google Scholar 

  39. Park, K.W., Bu, S.J., Cho, S.B.: Evolutionary optimization of neuro-symbolic integration for phishing URL detection. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 88–100. Springer (2021)

    Google Scholar 

  40. Shakya, A., Rus, V., Venugopal, D.: Student strategy prediction using a neuro-symbolic approach. In: International Educational Data Mining Society (2021)

    Google Scholar 

  41. Wang, W., Yang, Y.: Towards data-and knowledge-driven artificial intelligence: A survey on neuro-symbolic computing. Preprint (2022). arXiv:2210.15889

    Google Scholar 

  42. Yang, Z., Ishay, A., Lee, J.: NeurASP: embracing neural networks into answer set programming. In: 29th International Joint Conference on Artificial Intelligence (IJCAI 2020) (2020)

    Google Scholar 

  43. Yang, S., Zhang, R., Erfani, S., Lau, J.H.: An interpretable neuro-symbolic reasoning framework for task-oriented dialogue generation. Preprint (2022). arXiv:2203.05843

    Google Scholar 

  44. Yi, K., Wu, J., Gan, C., Torralba, A., Kohli, P., Tenenbaum, J.: Neural-symbolic VQA: disentangling reasoning from vision and language understanding. Adv. Neural Inf. Process. Syst. 31 (2018)

    Google Scholar 

  45. Zheng, K., Zhou, K., Gu, J., Fan, Y., Wang, J., Li, Z., He, X., Wang, X.E.: Jarvis: a neuro-symbolic commonsense reasoning framework for conversational embodied agents. Preprint (2022). arXiv:2208.13266

    Google Scholar 

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Authors and Affiliations

  1. Arizona State University, Tempe, AZ, USA

    Paulo Shakarian, Chitta Baral, Bowen Xi & Lahari Pokala

  2. Department of Computer Science and Engineering, Universidad Nacional del Sur (UNS), Institute for Computer Science and Engineering (UNS-CONICET), Bahía Blanca, Argentina

    Gerardo I. Simari

Authors
  1. Paulo Shakarian
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  2. Chitta Baral
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  3. Gerardo I. Simari
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  4. Bowen Xi
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  5. Lahari Pokala
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Shakarian, P., Baral, C., Simari, G.I., Xi, B., Pokala, L. (2023). Neuro Symbolic Applications. In: Neuro Symbolic Reasoning and Learning. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-031-39179-8_11

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  • DOI: https://doi.org/10.1007/978-3-031-39179-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-39178-1

  • Online ISBN: 978-3-031-39179-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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