Real-world verification techniques for Robotic and Embedded BDI agents: A systematic mapping
Abstract
An agent is an autonomous software entity capable of cognition, proactivity, and adaptability. A group of agents collaborating, cooperating, or competing between themselves is called a Multiagent System (MAS). MAS are widely adopted for robotic and embedded systems as their natural autonomy is well suited to these systems. Given the costs associated with real hardware construction, it is a common practice to use simulators in their development before deploying to the real world. However, simulations may not get a full account of what will happen in the real world. This work presents an ongoing work on methods to verify and guarantee behaviors observed in simulations happen as well on the real world of embedded and robotic MAS.
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Bratman, M. E., Israel, D. J., and Pollack, M. E. (1988). Plans and resource-bounded practical reasoning. Computational Intelligence, 4(3):349–355.
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Gao, Y., Chang, D., Chen, C.-H., and Xu, Z. (2022). Design of digital twin applications in automated storage yard scheduling. Advanced Engineering Informatics, 51:101477.
Karaduman, B., Tezel, B., and Challenger, M. (2024). On the impact of fuzzy-logic based BDI agent model for cyber–physical systems[Formula presented]. Expert Systems with Applications, 238.
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, pages 48–71, Cham. Springer Nature Switzerland.
Marah, H. and Challenger, M. (2024). Adaptive hybrid reasoning for agent-based digital twins of distributed multi-robot systems. SIMULATION, 100(9):931–957. Publisher: SAGE Publications Ltd STM.
Montoya-Zapata, S., Klement, N., Silva, C., Gibaru, O., and Lafou, M. (2024). Multi-agent system for perturbations in the kitting process of an automotive assembly line. Engineering Applications of Artificial Intelligence, 135:108679.
Murray, Y., Sirevåg, M., Ribeiro, P., Anisi, D. A., and Mossige, M. (2022). Safety assurance of an industrial robotic control system using hardware/software co-verification. Science of Computer Programming, 216:102766.
Patra, S., Mason, J., Ghallab, M., Nau, D., and Traverso, P. (2021). Deliberative acting, planning and learning with hierarchical operational models. Artificial Intelligence, 299:103523.
Robotics, O. (2025). ROS: Home.
Sichman, J. S., Demazeau, Y., and Boissier, O. (1992). When can knowledge-based systems be called agents? In Proceedings of the IX Brazilian Symposium on Artificial Intelligence (SBIA), volume 9, pages 172–185, Rio de Janeiro. SBC.
Staroletov, S., Shilov, N., Zyubin, V., Liakh, T., Rozov, A., Konyukhov, I., Shilov, I., Baar, T., and Schulte, H. (2019). Model-driven methods to design of reliable multiagent cyber-physical systems. volume 2478, pages 74–91.
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Published
2025-09-29
How to Cite
FREITAS, Bruno Policarpo Toledo; PANTOJA, Carlos Eduardo; VITERBO, José.
Real-world verification techniques for Robotic and Embedded BDI agents: A systematic mapping. In: WORKSHOP-SCHOOL ON AGENTS, ENVIRONMENTS, AND APPLICATIONS (WESAAC), 19. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 145-150.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2025.37551.
