Classificação de Trabalhos em Inteligência Artificial Neuro-Simbólica a partir das Taxonomias de Kautz e de Bader & Hitzler
Resumo
A Inteligência Artificial Neuro-Simbólica (NeSy AI) busca integrar o aprendizado neural com a explicabilidade do raciocínio simbólico, aliando desempenho e interpretabilidade. Contudo, a diversidade de arquiteturas dificulta comparações e a construção de um panorama claro da área. Este trabalho analisa dez aplicações neuro-simbólicas, selecionadas por mapeamento sistemático da literatura, e as classifica segundo dois modelos: a taxonomia de Bader & Hitzler (2005), que avalia inter-relação entre componentes, tipo de linguagem simbólica e finalidade da aplicação; e a proposta de Kautz (2021), que organiza sistemas em seis arquiteturas de integração. Os resultados mostram a predominância de abordagens híbridas, o uso frequente de linguagens proposicionais e o destaque para o design Neuro(Symbolic). A análise oferece uma visão mais clara da área ao revelar tendências e lacunas, além de orientar pesquisas e classificações futuras de sistemas neuro-simbólicos.
Palavras-chave:
euro-Simbólico, Inteligência Artificial e Taxonomia
Referências
Ahmed, K., Teso, S., Chang, K.-W., den Broeck, G. V., and Vergari, A. (2022). Semantic probabilistic layers for neuro-symbolic learning. Advances in Neural Information Processing Systems.
Amizadeh, S., Palangi, H., Polozov, O., Huang, Y., and Koishida, K. (2020). Neuro-symbolic visual reasoning: Disentangling ”visual”from ”reasoning”. 37th International Conference on Machine Learning, ICML 2020.
Bader, S. and Hitzler, P. (2005). Dimensions of neural-symbolic integration - a structured survey. arXiv:cs/0511042. DOI: 10.48550/arXiv.cs/0511042
Bosselut, A., Bras, R. L., and Choi, Y. (2021). Dynamic neuro-symbolic knowledge graph construction for zero-shot commonsense question answering. 35th AAAI Conference on Artificial Intelligence, AAAI 2021. DOI: 10.1609/aaai.v35i6.16625
Chitnis, R., Silver, T., Tenenbaum, J. B., Lozano-Perez, T., and Kaelbling, L. P. (2022). Learning neuro-symbolic relational transition models for bilevel planning. IEEE International Conference on Intelligent Robots and Systems. DOI: 10.1109/IROS47612.2022.9981440
Dennis, L., Farrell, M., and Fisher, M. (2023). Developing multi-agent systems with degrees of neuro-symbolic integration. arXiv:2305.11534v1. DOI: 10.48550/arXiv.2305.11534
Glanois, C., Feng, X., Jiang, Z., Weng, P., Zimmer, M., Li, D., and Liu, W. (2022). Neurosymbolic hierarchical rule induction. Proceedings of Machine Learning Research.
Hagos, Haileselassie, D., Rawat, and B., D. (2024). Neuro-symbolic ai for military applications. IEEE Transactions on Artificial Intelligence. DOI: 10.1109/TAI.2024.3444746
Hamilton, K., Nayak, A., Božić, B., and Longo, L. (2024). Is neuro-symbolic ai meeting its promises in natural language processing? a structured review. Semantic Web Volume 15, Issue 4: Neuro-Symbolic Artificial Intelligence and the Semantic Web.
Hsu, J., Mao, J., and Wu, J. (2023). Ns3d: Neuro-symbolic grounding of 3d objects and relations. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. DOI: 10.1109/CVPR52729.2023.00257
Kautz, H. A. (2021). The third ai summer: Aaai robert s. engelmore memorial lecture. AI Magazine 43: 105–125. DOI: 10.1002/aaai.12036
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001.
Kocoń, J., Baran, J., Gruza, M., Janz, A., Kajstura, M., Kazienko, P., Korczyński, W., Miłkowski, P., Piasecki, M., and Szołomicka, J. (2022). Neuro-symbolic models for sentiment analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). DOI: 10.1007/978-3-031-08754-7_69
Nye, M., Tessler, M. H., Tenenbaum, J. B., and Lake, B. M. (2021). Improving coherence and consistency in neural sequence models with dual-system, neuro-symbolic reasoning. Advances in Neural Information Processing Systems.
Saker, M. K., Zhou, L., Eberhart, A., and Hitzler, P. (2021). Neuro-symbolic artificial intelligence current trends. arXiv:2105.05330v1. DOI: 10.48550/arXiv.2105.05330
van Bekkum, M., de Boer, M., van Harmelen, F., Meyer-Vitali, A., and ten Teije, A. (2021). Modular design patterns for hybrid learning and reasoning systems: a taxonomy, patterns and use cases. DOI: 10.1007/s10489-021-02394-3
Wang, W., Yang, Y., and Wu, F. (2024). Towards data-and knowledge-driven artificial intelligence: A survey on neuro-symbolic computing. DOI: 10.1109/TPAMI.2024.3483273
Zellers, R., Holtzman, A., Peters, M., Mottaghi, R., Kembhavi, A., Farhadi, A., and Choi, Y. (2022). Piglet: Language grounding through neuro-symbolic interaction in a 3d world. ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference. DOI: 10.18653/v1/2021.acl-long.159
Škrlj B., M., M., N., L., and S., P. (2021). autobot: evolving neuro-symbolic representations for explainable low resource text classification. Springer Machine Learning Volume 110, Issue 5, May 2021, Pages 989-1028. DOI: 10.1007/s10994-021-05968-x
Amizadeh, S., Palangi, H., Polozov, O., Huang, Y., and Koishida, K. (2020). Neuro-symbolic visual reasoning: Disentangling ”visual”from ”reasoning”. 37th International Conference on Machine Learning, ICML 2020.
Bader, S. and Hitzler, P. (2005). Dimensions of neural-symbolic integration - a structured survey. arXiv:cs/0511042. DOI: 10.48550/arXiv.cs/0511042
Bosselut, A., Bras, R. L., and Choi, Y. (2021). Dynamic neuro-symbolic knowledge graph construction for zero-shot commonsense question answering. 35th AAAI Conference on Artificial Intelligence, AAAI 2021. DOI: 10.1609/aaai.v35i6.16625
Chitnis, R., Silver, T., Tenenbaum, J. B., Lozano-Perez, T., and Kaelbling, L. P. (2022). Learning neuro-symbolic relational transition models for bilevel planning. IEEE International Conference on Intelligent Robots and Systems. DOI: 10.1109/IROS47612.2022.9981440
Dennis, L., Farrell, M., and Fisher, M. (2023). Developing multi-agent systems with degrees of neuro-symbolic integration. arXiv:2305.11534v1. DOI: 10.48550/arXiv.2305.11534
Glanois, C., Feng, X., Jiang, Z., Weng, P., Zimmer, M., Li, D., and Liu, W. (2022). Neurosymbolic hierarchical rule induction. Proceedings of Machine Learning Research.
Hagos, Haileselassie, D., Rawat, and B., D. (2024). Neuro-symbolic ai for military applications. IEEE Transactions on Artificial Intelligence. DOI: 10.1109/TAI.2024.3444746
Hamilton, K., Nayak, A., Božić, B., and Longo, L. (2024). Is neuro-symbolic ai meeting its promises in natural language processing? a structured review. Semantic Web Volume 15, Issue 4: Neuro-Symbolic Artificial Intelligence and the Semantic Web.
Hsu, J., Mao, J., and Wu, J. (2023). Ns3d: Neuro-symbolic grounding of 3d objects and relations. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. DOI: 10.1109/CVPR52729.2023.00257
Kautz, H. A. (2021). The third ai summer: Aaai robert s. engelmore memorial lecture. AI Magazine 43: 105–125. DOI: 10.1002/aaai.12036
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001.
Kocoń, J., Baran, J., Gruza, M., Janz, A., Kajstura, M., Kazienko, P., Korczyński, W., Miłkowski, P., Piasecki, M., and Szołomicka, J. (2022). Neuro-symbolic models for sentiment analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). DOI: 10.1007/978-3-031-08754-7_69
Nye, M., Tessler, M. H., Tenenbaum, J. B., and Lake, B. M. (2021). Improving coherence and consistency in neural sequence models with dual-system, neuro-symbolic reasoning. Advances in Neural Information Processing Systems.
Saker, M. K., Zhou, L., Eberhart, A., and Hitzler, P. (2021). Neuro-symbolic artificial intelligence current trends. arXiv:2105.05330v1. DOI: 10.48550/arXiv.2105.05330
van Bekkum, M., de Boer, M., van Harmelen, F., Meyer-Vitali, A., and ten Teije, A. (2021). Modular design patterns for hybrid learning and reasoning systems: a taxonomy, patterns and use cases. DOI: 10.1007/s10489-021-02394-3
Wang, W., Yang, Y., and Wu, F. (2024). Towards data-and knowledge-driven artificial intelligence: A survey on neuro-symbolic computing. DOI: 10.1109/TPAMI.2024.3483273
Zellers, R., Holtzman, A., Peters, M., Mottaghi, R., Kembhavi, A., Farhadi, A., and Choi, Y. (2022). Piglet: Language grounding through neuro-symbolic interaction in a 3d world. ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference. DOI: 10.18653/v1/2021.acl-long.159
Škrlj B., M., M., N., L., and S., P. (2021). autobot: evolving neuro-symbolic representations for explainable low resource text classification. Springer Machine Learning Volume 110, Issue 5, May 2021, Pages 989-1028. DOI: 10.1007/s10994-021-05968-x
Publicado
10/09/2025
Como Citar
LEPINSK, João P.; DERKASCZ, Thomas K.; CARDOSO, Rafael C.; BORGES, André P.; ALVES, Gleifer V..
Classificação de Trabalhos em Inteligência Artificial Neuro-Simbólica a partir das Taxonomias de Kautz e de Bader & Hitzler. In: WORKSHOP-ESCOLA DE INFORMÁTICA TEÓRICA (WEIT), 8. , 2025, Ponta Grossa/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 64-73.
