Integrating Simulated Exogenous Environments to Support the Learning Process of the Embedded MAS Approach
Resumo
A multiagent system (MAS) is a group of autonomous software entities capable of collaboration and cooperation. It is a common practice to use simulators in MAS development. However, in the case of Embedded MAS education, integrating simulators is difficult because the agent’s reasoning is tightly coupled to hardware communication interfaces. This work then presents a simulator integration methodology based on serial channel emulation that fully decouples the agent’s reasoning from the environment, allowing flexible integration of simulators. We present a report on an Embedded AI undergraduate course in two academic semesters, showing that the student’s projects completion rate increased from 33% to 100%.
Referências
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Lazarin, N., Pantoja, C., and Viterbo, J. (2023). Towards a Toolkit for Teaching AI Supported by Robotic-agents: Proposal and First Impressions. In Proceedings of XXXI Workshop sobre Educação em Computação (WEI 2023), Porto Alegre. SBC. DOI: 10.5753/wei.2023.229753.
Lazarin, N. M., Pantoja, C. E., and Viterbo, J. (2024). Dealing with the Unpredictability of Physical Resources in Real-World Multi-agent Systems. In Rocha, A. P., Steels, L., and Van Den Herik, J., editors, Agents and Artificial Intelligence, volume 14546, pages 48–71, Cham. Springer Nature Switzerland. DOI: 10.1007/978-3-031-55326-4_3.
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Pantoja, C. E., Jesus, V. S. D., Lazarin, N. M., and Viterbo, J. (2023). A Spin-off Version of Jason for IoT and Embedded Multi-Agent Systems. In Intelligent Systems, volume 14195, pages 382–396. Springer Nature Switzerland, Cham. DOI: 10.1007/978-3-031-45368-7_25.
Pantoja, C. E., Stabile, M. F., Lazarin, N. M., and Sichman, J. S. (2016). ARGO: An Extended Jason Architecture that Facilitates Embedded Robotic Agents Programming. In Engineering Multi-Agent Systems, pages 136–155. Springer, Cham. DOI: 10.1007/978-3-319-50983-9_8.
Ricci, A., Santi, A., and Piunti, M. (2012). Action and Perception in Agent Programming Languages: From Exogenous to Endogenous Environments. In Programming Multi-Agent Systems, pages 119–138. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-28939-2_7.
Sakellariou, I., Kefalas, P., and Stamatopoulou, I. (2008). Teaching Intelligent Agents using NetLogo. In Proceedings of the ACM-IFIP IEEIII 2008 - Informatics Education Europe III Conference, pages 209–221, Venice. Ca’ Foscari. URL: [link].
Singh, D., Padgham, L., and Logan, B. (2016). Integrating BDI Agents with Agent-Based Simulation Platforms. Autonomous Agents and Multi-Agent Systems, 30(6):1050–1071.
Souza De Jesus, V., Mori Lazarin, N., Pantoja, C. E., Vaz Alves, G., Ramos Alves De Lima, G., and Viterbo, J. (2023). An IDE to Support the Development of Embedded Multi-Agent Systems. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. Springer, Cham. DOI: 10.1007/978-3-031-37616-0_29.
Yim, I. H. Y. and Su, J. (2024). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. Journal of Computers in Education. DOI: 10.1007/s40692-023-00304-9.
Bordini, R. H. and Hübner, J. F. (2006). BDI Agent Programming in AgentSpeak Using Jason. In Computational Logic in Multi-Agent Systems. Springer, Berlin. DOI: 10.1007/11750734_9.
Bratman, M. E., Israel, D. J., and Pollack, M. E. (1988). Plans and resource-bounded practical reasoning. Computational Intelligence, 4(3). DOI: 10.1111/j.1467-8640.1988.tb00284.x.
Bravo, F. A. and Páez, J. J. (2023). Exploring the use of MAS in Educational Robotics Activities. IEEE Transactions on Learning Technologies, 16(6). DOI: 10.1109/TLT.2023.3277715.
Burattini, S., Ricci, A., Mayer, S., Vachtsevanou, D., Lemee, J., Ciortea, A., and Croatti, A. (2022). Agent-Oriented Visual Programming for the Web of Things. In Proceedings EMAS 2022, Auckland, New Zealand. IFAAMAS. DOI: 20.500.14171/109205.
Davoust, A., Gavigan, P., Ruiz-Martin, C., Trabes, G., Esfandiari, B., Wainer, G., and James, J. (2020). An Architecture for Integrating BDI Agents with a Simulation Environment. In Dennis, L. A., Bordini, R. H., and Lespérance, Y., editors, Engineering Multi-Agent Systems, pages 67–84, Cham. Springer International Publishing.
Freitas, B., Lazarin, N., and Pantoja, C. (2023). A Proposal for a Serial Port Emulator for Embedded Multi-Agent Systems. In Proceedings of the 17th Workshop-School on Agents, Environments, and Applications, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/wesaac.2023.33437.
Gavigan, P. and Esfandiari, B. (2021a). Agent in a Box: A Framework for Autonomous Mobile Robots with BDI. Electronics, 10(17). DOI: 10.3390/electronics10172136.
Gavigan, P. and Esfandiari, B. (2021b). Agent in a Box: A Framework for Autonomous Mobile Robots with Beliefs, Desires, and Intentions. Electronics, 10(17):2136.
González, S. (2023). SimulIDE Circuit Simulator. [link].
Karaduman, B., Tezel, B. T., and Challenger, M. (2023). Rational software agents with the BDI reasoning model for Cyber–Physical Systems. Engineering Applications of Artificial Intelligence, 123. DOI: 10.1016/j.engappai.2023.106478.
Lazarin, N. and Pantoja, C. (2015). A Robotic-agent Platform For Embedding Software Agents using Raspberry Pi and Arduino Boards. In Proceedings of the 9th Workshop-School on Agents, Environments, and Applications, Porto Alegre, RS. SBC. DOI: 10.5753/wesaac.2015.33308.
Lazarin, N., Pantoja, C., and Viterbo, J. (2023). Towards a Toolkit for Teaching AI Supported by Robotic-agents: Proposal and First Impressions. In Proceedings of XXXI Workshop sobre Educação em Computação (WEI 2023), Porto Alegre. SBC. DOI: 10.5753/wei.2023.229753.
Lazarin, N. M., Pantoja, C. E., and Viterbo, J. (2024). Dealing with the Unpredictability of Physical Resources in Real-World Multi-agent Systems. In Rocha, A. P., Steels, L., and Van Den Herik, J., editors, Agents and Artificial Intelligence, volume 14546, pages 48–71, Cham. Springer Nature Switzerland. DOI: 10.1007/978-3-031-55326-4_3.
Linux Kernel Organization, Inc. (2023). TTY — The Linux Kernel documentation. [link].
Michel, F., Ferber, J., and Drogoul, A. (2009). Multi-Agent Systems and Simulation: A Survey from the Agent Community’s Perspective. In Multi-Agent systems: Simulation and applications. CRC Press.
Michel, O. (1998). Webots: Symbiosis Between Virtual and Real Mobile Robots. In Goos, G., Hartmanis, J., Van Leeuwen, J., and Heudin, J.-C., editors, Virtual Worlds, volume 1434, pages 254–263. Springer, Berlin, Heidelberg. DOI: 10.1007/3-540-68686-X_24.
Onyedinma, C., Gavigan, P., and Esfandiari, B. (2020). Toward Campus Mail Delivery Using BDI. Journal of Sensor and Actuator Networks, 9(4):56. DOI: 10.3390/jsan9040056.
Palanca, J., Jordán, J., and Julián, V. (2021). Using learning by doing methodology for teaching multi-agent systems. In INTED2021 Proceedings. DOI: 10.21125/inted.2021.0794.
Palanca, J., Terrasa, A., Julian, V., and Carrascosa, C. (2020). SPADE 3: Supporting the New Generation of Multi-Agent Systems. IEEE Access, 8. DOI: 10.1109/ACCESS.2020.3027357.
Pantoja, C. E., Jesus, V. S. D., Lazarin, N. M., and Viterbo, J. (2023). A Spin-off Version of Jason for IoT and Embedded Multi-Agent Systems. In Intelligent Systems, volume 14195, pages 382–396. Springer Nature Switzerland, Cham. DOI: 10.1007/978-3-031-45368-7_25.
Pantoja, C. E., Stabile, M. F., Lazarin, N. M., and Sichman, J. S. (2016). ARGO: An Extended Jason Architecture that Facilitates Embedded Robotic Agents Programming. In Engineering Multi-Agent Systems, pages 136–155. Springer, Cham. DOI: 10.1007/978-3-319-50983-9_8.
Ricci, A., Santi, A., and Piunti, M. (2012). Action and Perception in Agent Programming Languages: From Exogenous to Endogenous Environments. In Programming Multi-Agent Systems, pages 119–138. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-28939-2_7.
Sakellariou, I., Kefalas, P., and Stamatopoulou, I. (2008). Teaching Intelligent Agents using NetLogo. In Proceedings of the ACM-IFIP IEEIII 2008 - Informatics Education Europe III Conference, pages 209–221, Venice. Ca’ Foscari. URL: [link].
Singh, D., Padgham, L., and Logan, B. (2016). Integrating BDI Agents with Agent-Based Simulation Platforms. Autonomous Agents and Multi-Agent Systems, 30(6):1050–1071.
Souza De Jesus, V., Mori Lazarin, N., Pantoja, C. E., Vaz Alves, G., Ramos Alves De Lima, G., and Viterbo, J. (2023). An IDE to Support the Development of Embedded Multi-Agent Systems. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. Springer, Cham. DOI: 10.1007/978-3-031-37616-0_29.
Yim, I. H. Y. and Su, J. (2024). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. Journal of Computers in Education. DOI: 10.1007/s40692-023-00304-9.
Publicado
20/07/2025
Como Citar
FREITAS, Bruno Policarpo Toledo; LAZARIN, Nilson Mori; PANTOJA, Carlos Eduardo; VITERBO, José.
Integrating Simulated Exogenous Environments to Support the Learning Process of the Embedded MAS Approach. In: WORKSHOP SOBRE EDUCAÇÃO EM COMPUTAÇÃO (WEI), 33. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1195-1206.
ISSN 2595-6175.
DOI: https://doi.org/10.5753/wei.2025.9115.
