USO DA INTELIGÊNCIA ARTIFICIAL NEUROSSIMBÓLICA NO PLANEJAMENTO ASSISTIVO DE CENTROS URBANOS INTELIGENTES

Autores

DOI:

https://doi.org/10.18623/rvd.v23.6205

Palavras-chave:

Bem-Estar Coletivo, Cidade Inteligente, IA Neurossimbólica, Planejamento e Desenvolvimento Regional

Resumo

A Inteligência Artificial Neurossimbólica no planejamento assistivo de centros urbanos inteligentes representa uma abordagem inovadora que combina o melhor das redes neurais com o raciocínio baseado em regras e símbolos. O diagnóstico da Inteligência Artificial ganhou significativa confiabilidade alcançando avanços notáveis. Contudo a caixa preta habilitada para IA não é interpretável e prejudica o planejamento de novos centros urbanos inteligentes e o entendimento do bem-estar coletivo dos indivíduos. Esta pesquisa objetiva abordar o uso da inteligência artificial neurossimbólica para tornar a caixa preta interpretável incorporando o conhecimento especializado para previsões confiáveis. A metodologia proposta contou com pesquisa bibliográfica e literatura técnica alusivas ao tema na definição da intepretação neurossimbólica da leitura facial, processamento dos sinais e tomadas de decisões. Um protótipo de aprendizado virtual foi simulado com base em reconhecimento de emoções primárias. Como resultado, a ampla divulgação do conceito da IANS para planejamento e desenvolvimento de centros urbanos inteligentes mostra-se essencial ao integrar bases neurais dos sistemas baseados em conhecimento treináveis e interpretáveis. Conclui-se que a transparência, capacidade e generalização para condições de planejamento ainda invisíveis e não interpretáveis podem ser tornar robustas com previsão interpretável no planejamento e desenvolvimento de centros urbanos inteligentes.

Referências

AMADOR-DOMÍNGUEZ, E., SERRANO, E.; MANRIQUE, D. (2024). Neuro- symbolic system profiling: A template-based approach. Knowledge-Based Systems, 287, 111441. https://doi.org/10.1016/j.knosys.2024.111441

ASPIS, Y., BRODA, K., LOBO, J., & RUSSO, A. (2022). Embed2Sym: Scalable neuro-symbolic reasoning via clustered embeddings. Proceedings of the 19th International Conf. on Princ. of Knowledge Representation and Reasoning. https://doi.org/10.24963/kr.2022/44

BADREDDINE, S., D’AVILA GARCEZ, A., SERAFINI, L., & SPRANGER, M. (2022). Logic Tensor Networks. Artificial Intelligence, 303, 103649. https://doi.org/10.1016/j.artint.2021.103649

BELLE, V. (2020). Symbolic logic meets machine learning: A brief survey in infinite domains. ArXiv Preprint. https://doi.org/10.1007/978-3-030-58449-8_1

BENITEZ-QUIROZ, C.F., SRINIVASAN, R., MARTINEZ, A.M. (2016). Emotion Net: an accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild, pp. 5562–5570

BESOLD, T. R.; GARCEZ, A. ; BADER, S.; BROWNE, W.; DE PENNING, L.; FREFIELD, J.; KUEHNBERGER, K.-U.; LAMB, L. C.; LEHMANN, J.; MIKKULAINEN, R.; TRAN, S. N. (2017). Neural-symbolic learning and reasoning: survey and interpretation. arXiv, [S. l.]. Disponível em: https://arxiv.org/pdf/1711.03902.pdf. Acesso em: jun. 2021.

CHACCOUR, C., KARAPANTELAKIS, A., MURPHY, T., & DOHLER, M. (2024). Telecom’s artificial general intelligence (AGI) vision: Beyond the GenAI frontier. IEEE Network, September/October 2024, 10–28. https://doi.org/10.1109/MNET.2024.3425594

CHEN, Z., DONG, S., YI, K., LI, Y., DING, M., TORRALBA, A., TENENBAUM, J. B., & GAN, C. (2025). Compositional physical reasoning of objects and events from videos. IEEE Trans. Pattern Analysis and Machine Intel. https://doi.org/10.1109/TPAMI.2025.3574322

CHITNIS, R., SILVER, T., TENENBAUM, J. B., LOZANO-PÉREZ, T., & KAELBLING, L. P. (2022). Learning neuro-symbolic relational transition models bilevel planning. IEEE Int. Conf. Rob-Syst (IROS) pp. 4166–4173. https://doi.org/10.1109/IROS47612.2022.9981440

CORCHADO, J. M., BORRAJO, M. L., PELLICER, M. A., & YÁÑEZ, J. C. (2004). Neuro-symbolic system for business internal control. Lecture Notes in Artificial Intelligence, 3275, 1–10. https://doi.org/10.1007/978-3-540-30185-1_1

CUSTODE, L. L., MENTO, F., TURSI, F., SMARGIASSI, A., INCHINGOLO, R., PERRONE, T., DEMI, L., & IACCA, G. (2023). Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees. Applied Soft Computing, 133, 109926. https://doi.org/10.1016/j.asoc.2022.109926

EBRAHIMI, M., EBERHART, A., BIANCHI, F., & HITZLER, P. (2021). Towards bridging the neuro-symbolic gap: Deep deductive reasoners. Applied Intelligence, 51, 6326–6348. https://doi.org/10.1007/s10489-020-02165-6

EDWARDS, D. J. (2024). A functional contextual, observer-centric, quantum mechanical, and neuro-symbolic approach to solving the alignment problem of artificial general intelligence: safe AI through intersecting computational psychological neuroscience and LLM architecture for emergent theory of mind. Frontiers in Computational Neuroscience, 18, 1395901. https://doi.org/10.3389/fncom.2024.1395901

EKMAN, P. (2011). A linguagem das emoções: revolucionando sua comunicação e seus relacionamentos reconhecendo todas as expressões das pessoas ao redor. São Paulo. Editora Lua de Papel. 1ª Edição. ISBN-10: 8563066420. ISBN-13: 978-8563066428. Pp. 288.

FELDSTEIN, J.; DILKAS, P.; BELLE, V.; TSAMOURA, E. (2023). Mapping the neuro symbolic ai landscape by architectures: a handbook on augmenting deep learning through symbolic reasoning. arXiv, [S. l.]. Disponível em: https://arxiv.org/abs/2410.22077. Acesso em: jul. 2025.DOI:10.48550/arXiv.2410.22077

GARCEZ, A. D. d’A.; BESOLD, T. R.; DE RAEDT, L.; FÖLDIAK, P.; HITZLER, P.; ICARD, T.; KÜHNBERGER, K.‑U.; LAMB, L. C.; MIIKKULAINEN, R.; SILVER, D. L. Neural‑symbolic learning and reasoning: Contributions and challenges. In: AAAI Spring Symposium on KRR: Integr. Symbolic and Neural Approaches, Technical report pp. 18-21, March 23–25, 2015. AAAI Press (2015). https://openaccess.city.ac.uk/id/eprint/11838/

GIUNCHIGLIA, E.; TATOMIR, A.; STOIAN, M. C.; LUKASIEWICZ, T. CCN+: A neuro-symbolic framework for deep learning with requirements. International Journal of Approximate Reasoning, v. 171, art. 109124, 2024. DOI: 10.1016/j.ijar.2024.109124.

GOUSSAIN¹, B. G. C. S.; ANDRADE, H. S.; et. al. (2025). Electrodermal activity as an indicator of student engagement: a comparative study of traditional and active learning environments. 23rd LACCEI International Multi-Conference for Engineering, Education, and Technology: “Engineering, Artificial Intelligence, and Sustainable Technologies in service of society”. Hybrid Event, Mexico City, July 16 - 18, 2025. SBN: 978-628-96613-1-6. ISSN: 2414-6390. DOI: https://dx.doi.org/10.18687/LACCEI2025.1.1.2423

GOUSSAIN², B. G. C. S., MOURA, R. A., LUCHE, J. R. D., et al. (2025). Enhancing learning with physiological measures: a systematic review of applications in neuroeducation. In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 1, pages 111-122 ISBN: 978-989-758-746-7; ISSN: 2184-5026. DOI: 10.5220/0013438400003932

HAMMER, B., HITZLER, P.: Perspectives of neural-symbolic integration. Studies in Computational Intelligence, vol. 77. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73954-8

HITZLER, P.; EBERHART, A.; EBRAHIMI, M.; SARKER, M. K.; ZHOU, L. (2022). Neuro symbolic approaches in artificial intelligence. National Science Review, v. 9, n. 6: nwac035, 2022. DOI: 10.1093/nsr/nwac035.

KOCOŃ, J., BARAN, J., GRUZA, M., JANZ, A., KAJSTURA, M., KAZIENKO, P., KORCZYŃSKI, W., MIŁKOWSKI, P., PIASECKI, M., & SZOŁOMICKA, J. (2022). Neuro-symbolic models for sentiment analysis. Lecture Notes in Computer Science, 133, 1653–1667. https://doi.org/10.1007/978-3-031-08754-7_69

LAU, B. P. L.; MARAKKALAGE, S.H.; ZHOU, Y.; HASSAN, N. U.; YUEN, C. ; ZHANG.; TAN, U-X. (2019). A survey of data fusion in smart city applications, Information Fusion, vol. 52, pp. 357–374, Dec. 2019. DOI: 10.1016/j.inffus.2019.05.004

LIAO, S. H. (2005). Expert system methodologies and applications: a decade review from 1995 to 2004. Expert Systems with Applications, v. 28, n. 1, p. 93–103, Jan. 2005. DOI: 10.1016/j.eswa.2004.08.003.

MAO, J., GAN, C., KOHLI, P., TENENBAUM, J. B., & WU, J. (2019). The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. ICLR Conference Paper. https://doi.org/10.48550/arXiv.1904.12584

MASSARETO, H. E. (2024). Inteligência artificial neuro-simbólica: integração entre lógica simbólica e redes neurais. Revista IANS. https://gecompany.com.br/educacional/criatividade-novo/inteligencia-artificial-neuro-simbolica-integracao-entre-logica-simbolica-e-redes-neurais

MIHINDUKULASOORIYA, N., TIWARI, S., ENGUIX, C. F., & LATA, K. (2023). Text2KGBench: A benchmark for ontology-driven knowledge graph generation from text. ArXiv Preprint. https://doi.org/10.1007/978-3-031-47243-5_14

MOREL, G. (2021). Neuro-symbolic A.I. for the smart city. Journal of Physics: Conference Series, 2042(1), 012018. DOI: 10.1088/1742-6596/2042/1/012018

MOURA, R., RICHETTO, M., LUCHE, D., TOZI, L. AND SILVA, M. (2022). New professional competencies and skills leaning towards Industry 4.0. In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 2, pages 622-630 ISBN: 978-989-758-562-3; ISSN: 2184-5026. DOI: 10.5220/0011047300003182

ONCHIS, D., ISTIN, C., & HOGEA, E. (2022). A neuro-symbolic classifier with optimized satisfiability for monitoring security alerts in network traffic. Applied Sciences, 12, 11502. https://doi.org/10.3390/app122211502

QOLOMANY, B.; AL FUQAHA, A.; GUPTA, A.; BENHADDOU, D.; ALWAJIDI, S.; QADIR, J.; FONG, A. C. F. (2019). Leveraging ML & Big data in smart build: a compreh. survey. IEEE Access v. 7, p. 90316–90356, DOI: 10.1109/ACCESS.2019.2926642.

RIAZ, M.N., SHEN, Y., SOHAIL, M., GUO, M. (2020). eXnet: an efficient approach for emotion recognition in the wild. Sensors 20(4), 1087 DOI: 10.3390/s20041087

SCHROFF, F., KALENICHENKO, D., PHILBIN, J. (2015). Face Net: a unified embedding for face recognition and clustering, pp. 815–823

SHILOV, N. G., PONOMAREV, A. V., & SMIRNOV, A. V. (2023). The analysis of ontology-based neuro-symbolic intelligence methods for collaborative decision support. Informatics and Automation, 22(3), 576–601. https://doi.org/10.15622/ia.22.3.4

SHOJAEILANGARI, S.; YAU, W. Y.; NANDAKUMAR, K.; LI, J.; TEOH, E. K. (2015). Robust representation and recognition of facial emotions using extreme sparse learning. IEEE Transactions on Image Proc., v. 24, n. 7, p. 2140-2152. DOI: 10.1109/TIP.2015.2416634.

SHUANGFENG, L. (2020). Tensor flow lite: on-device machine learning framework[J]. Journal of Computer Research and Development, 2020, 57(9): 1839-1853. DOI: 10.7544/issn1000-1239.2020.20200291

SIROVICH, L.; KIRBY, M. (1987). Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A, v. 4, n. 3, p. 519–524, 1987. DOI: 10.1364/JOSAA.4.000519.

SIYAEV, A.; JO, G-S. (2021). Neuro-symbolic speech understanding in aircraft maintenance metaverse. IEEE Access, 9, 154484-497. https://doi.org/10.1109/ACCESS.2021.3128616

SUN, J.; SUN, H.; HAN, T.; ZHOU, B. Neuro‑symbolic program search for autonomous driving decision module design. In: Proceedings of the 2020 Conference on Robot Learning. PMLR, vol. 155, pp. 21–30, 2021.

SUN, J. J.; TJANDRASUWITA, M.; SEHGAL, A.; SOLAR‑LEZAMA, A.; CHAUDHURI, S.; YUE, Y.; COSTILLA‑REYES, O. Neuro-symbolic Programming for Science. In: AI for Science Workshop, NeurIPS 2022. CoRR, vol. abs/2210.05050, 2022. Disponível em: https://doi.org/10.48550/arXiv.2210.05050. Acesso em: jul. 2025.

VAN BEKKUM, M.; DE BOER, M.; VAN HARMELEN, F.; MEYER‑VITALI, A.; TEN TEIJE, A. Modular design patterns for hybrid learning and reasoning systems: a taxonomy, patterns and use cases. Applied Intelligence, v. 51, n. 9, p. 6528–6546, set. 2021. DOI: 10.1007/s10489‑021‑02394‑3.

WANG, K.; PENG, X.; YANG, J.; MENG, D.; QIAO, Y. Region attention networks for pose and occlusion robust facial expression recognition. IEEE Transactions on Image Processing, vol. 29, pp. 4057–4069, 2020. DOI: 10.1109/TIP.2019.2956143.

YANG, C., MEI, H., & EISNER, J. (2022). Transformer embeddings of irregularly spaced event & their participants. ICLR. Proceedings. https://doi.org/10.48550/arXiv.2201.00044

ZHANG, K.; ZHANG, Z.; LI, Z.; QIAO, Y. (2016). Joint Face Detection and Alignment Using Multi-task Cascaded Convolutional Networks. IEEE Signal Processing Letters, v. 23, n. 10, p. 1499–1503, out. 2016. DOI: 10.1109/LSP.2016.2603342.

ZHAO, S., YOU, H., ZHANG, R.-Y., SI, B., ZHEN, Z., WAN, X., & WANG, D.-H. (2023). An interpretable neuro-symbolic model for Raven’s Progressive Matrices reasoning. Cognitive Computation, 15, 1703–1724. https://doi.org/10.1007/s12559-023-10154-3

Downloads

Publicado

2026-05-07

Como Citar

Benevides, M. P., Oliveira, M. R. de, & Moura, R. A. de. (2026). USO DA INTELIGÊNCIA ARTIFICIAL NEUROSSIMBÓLICA NO PLANEJAMENTO ASSISTIVO DE CENTROS URBANOS INTELIGENTES . Veredas Do Direito , 23(7), e236205. https://doi.org/10.18623/rvd.v23.6205