Revisão Sistemática da Literatura integrando Sistemas Multiagente e Large Language Models
Resumo
A revisão sistemática da literatura investiga os estudos realizados em uma determinada área. Desta forma, busca focar em um tema bem definido com intuito de identificar, selecionar, avaliar e sintetizar evidências de um tópico de pesquisa específico. A revisão sistemática da literatura possui como objetivo proporcionar uma visão ampla sobre um problema, com um detalhamento padronizado sobre os estudos científicos. Este estudo apresenta uma revisão sistemática de duas áreas: os Sistemas Multiagente (SMA) e os Large Language Models (LLMs), visto que a integração dessas áreas pode contribuir em demandas de problemas reais em diversas áreas do conhecimento.
Referências
Alvares, L. H. T. and Sichman, J. S. (1997). Introdução aos sistemas multiagentes. In Jornada de Atualização em Informática, 16.; Congresso da SBC, 17., Brasília, pages 1–38. SBC.
Boissier, O., Hübner, J. F., and Ricci, A. (2016). The JaCaMo Framework, pages 125–151. Springer International Publishing, Cham.
Born, M., Aguiar, M., and Adamatti, D. (2023). Modeling organizational level of agents with moise+ to a natural resources management rpg. In The international FLAIRS conference proceedings, volume 36.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. (2020). Language models are few-shot learners. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS ’20, Red Hook, NY, USA. Curran Associates Inc.
Chen, B., Li, G., Lin, X., Wang, Z., and Li, J. (2024). Blockagents: Towards byzantine-robust llm-based multi-agent coordination via blockchain. In Proceedings of the ACM Turing Award Celebration Conference-China 2024, pages 187–192.
Chen, W.-H., Tong, W., Case, A., and Zhang, T. (2025). Dango: A mixed-initiative data wrangling system using large language model. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25, New York, NY, USA. Association for Computing Machinery.
Cinkusz, K. and Chudziak, J. A. (2024). Towards llm-augmented multiagent systems for agile software engineering. In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, pages 2476–2477.
Fatemi, S. and Hu, Y. (2024). Finvision: A multi-agent framework for stock market prediction. In Proceedings of the 5th ACM International Conference on AI in Finance, ICAIF ’24, page 582–590, New York, NY, USA. Association for Computing Machinery.
Girardi, R. (2004). Introdução aos sistemas multiagentes. Dev-Media. Disponível em: [link].
Kitchenham, B. A. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report, United Kingdom.
Li, Z., Zhang, R., Wang, Z., Xie, Z., and Song, Y. (2025). Llm-guided decision-making toolkit for multi-agent reinforcement learning. Neurocomputing, 638:130105.
Orner, D., Ondula, E. A., Mumero Mwangi, N., and Goyal, R. (2024). Sentimental agents: Combining sentiment analysis and non-bayesian updating for cooperative decision-making. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS ’24, page 2408–2410, Richland, SC. International Foundation for Autonomous Agents and Multiagent Systems.
Ozdemir, S. (2023). Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs. Addison-Wesley, Boston, MA, USA.
Qiao, T., Walker, C., Cunningham, C., and Koh, Y. S. (2025). Thematic-lm: A llm-based multi-agent system for large-scale thematic analysis. In Proceedings of the ACM on Web Conference 2025, WWW ’25, page 649–658, New York, NY, USA. Association for Computing Machinery.
Russell, S. and Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson Education, Hoboken, NJ, USA, 4th edition.
Ryu, J., Kim, K., Heo, D., Song, H., Oh, C., and Suh, B. (2025). Cinema multiverse lounge: Enhancing film appreciation via multi-agent conversations. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25, New York, NY, USA. Association for Computing Machinery.
Shanahan, M. (2024). Talking about large language models. Communications of the ACM, 67(2):68–79.
Shi, J., Zhao, J., Wu, X., Xu, R., Jiang, Y.-H., and He, L. (2025). Mitigating reasoning hallucination through multi-agent collaborative filtering. Expert Systems with Applications, 263:125723.
Taillandier, P., Gaudou, B., Grignard, A., Quang Nghi, H., Marilleau, N., Caillou, P., Philippon, D., and Drogoul, A. (2019). Building, composing and experimenting complex spatial models with the gama platform. GeoInformatica, 23.
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W. (2023). Large language models in medicine. Nature medicine, 29(8):1930–1940.
Wang, Y., Wang, J., and Chu, Z. (2025a). Multi-agent large language models as evolutionary optimizers for scheduling optimization. Computers Industrial Engineering, 206:111197.
Wang, Z., Zhao, Z., Lyu, Y., Chen, Z., de Rijke, M., and Ren, Z. (2025b). A cooperative multi-agent framework for zero-shot named entity recognition. In Proceedings of the ACM on Web Conference 2025, WWW ’25, page 4183–4195, New York, NY, USA. Association for Computing Machinery.
Wooldridge, M. (2009). An Introduction to MultiAgent Systems. Wiley, Chichester, West Sussex, England, 2nd edition.
Yue, L., Xing, S., Chen, J., and Fu, T. (2024). Clinicalagent: Clinical trial multi-agent system with large language model-based reasoning. In Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB ’24, New York, NY, USA. Association for Computing Machinery.
Boissier, O., Hübner, J. F., and Ricci, A. (2016). The JaCaMo Framework, pages 125–151. Springer International Publishing, Cham.
Born, M., Aguiar, M., and Adamatti, D. (2023). Modeling organizational level of agents with moise+ to a natural resources management rpg. In The international FLAIRS conference proceedings, volume 36.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. (2020). Language models are few-shot learners. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS ’20, Red Hook, NY, USA. Curran Associates Inc.
Chen, B., Li, G., Lin, X., Wang, Z., and Li, J. (2024). Blockagents: Towards byzantine-robust llm-based multi-agent coordination via blockchain. In Proceedings of the ACM Turing Award Celebration Conference-China 2024, pages 187–192.
Chen, W.-H., Tong, W., Case, A., and Zhang, T. (2025). Dango: A mixed-initiative data wrangling system using large language model. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25, New York, NY, USA. Association for Computing Machinery.
Cinkusz, K. and Chudziak, J. A. (2024). Towards llm-augmented multiagent systems for agile software engineering. In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, pages 2476–2477.
Fatemi, S. and Hu, Y. (2024). Finvision: A multi-agent framework for stock market prediction. In Proceedings of the 5th ACM International Conference on AI in Finance, ICAIF ’24, page 582–590, New York, NY, USA. Association for Computing Machinery.
Girardi, R. (2004). Introdução aos sistemas multiagentes. Dev-Media. Disponível em: [link].
Kitchenham, B. A. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report, United Kingdom.
Li, Z., Zhang, R., Wang, Z., Xie, Z., and Song, Y. (2025). Llm-guided decision-making toolkit for multi-agent reinforcement learning. Neurocomputing, 638:130105.
Orner, D., Ondula, E. A., Mumero Mwangi, N., and Goyal, R. (2024). Sentimental agents: Combining sentiment analysis and non-bayesian updating for cooperative decision-making. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS ’24, page 2408–2410, Richland, SC. International Foundation for Autonomous Agents and Multiagent Systems.
Ozdemir, S. (2023). Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs. Addison-Wesley, Boston, MA, USA.
Qiao, T., Walker, C., Cunningham, C., and Koh, Y. S. (2025). Thematic-lm: A llm-based multi-agent system for large-scale thematic analysis. In Proceedings of the ACM on Web Conference 2025, WWW ’25, page 649–658, New York, NY, USA. Association for Computing Machinery.
Russell, S. and Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson Education, Hoboken, NJ, USA, 4th edition.
Ryu, J., Kim, K., Heo, D., Song, H., Oh, C., and Suh, B. (2025). Cinema multiverse lounge: Enhancing film appreciation via multi-agent conversations. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25, New York, NY, USA. Association for Computing Machinery.
Shanahan, M. (2024). Talking about large language models. Communications of the ACM, 67(2):68–79.
Shi, J., Zhao, J., Wu, X., Xu, R., Jiang, Y.-H., and He, L. (2025). Mitigating reasoning hallucination through multi-agent collaborative filtering. Expert Systems with Applications, 263:125723.
Taillandier, P., Gaudou, B., Grignard, A., Quang Nghi, H., Marilleau, N., Caillou, P., Philippon, D., and Drogoul, A. (2019). Building, composing and experimenting complex spatial models with the gama platform. GeoInformatica, 23.
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W. (2023). Large language models in medicine. Nature medicine, 29(8):1930–1940.
Wang, Y., Wang, J., and Chu, Z. (2025a). Multi-agent large language models as evolutionary optimizers for scheduling optimization. Computers Industrial Engineering, 206:111197.
Wang, Z., Zhao, Z., Lyu, Y., Chen, Z., de Rijke, M., and Ren, Z. (2025b). A cooperative multi-agent framework for zero-shot named entity recognition. In Proceedings of the ACM on Web Conference 2025, WWW ’25, page 4183–4195, New York, NY, USA. Association for Computing Machinery.
Wooldridge, M. (2009). An Introduction to MultiAgent Systems. Wiley, Chichester, West Sussex, England, 2nd edition.
Yue, L., Xing, S., Chen, J., and Fu, T. (2024). Clinicalagent: Clinical trial multi-agent system with large language model-based reasoning. In Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB ’24, New York, NY, USA. Association for Computing Machinery.
Publicado
29/09/2025
Como Citar
BORN, Míriam B.; ALVES, Bruno C.; GOULART, Felipe M.; CALDAS, Leticia B.; AGUIAR, Marilton S. de.
Revisão Sistemática da Literatura integrando Sistemas Multiagente e Large Language Models. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 19. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 230-239.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2025.37538.
