Swapping Physical Resources at Runtime in Embedded MultiAgent Systems
Demonstration of the paper accepted at the 15th International Conference on Agents and Artificial Intelligence (ICAART 2023).
An Embedded Multiagent System (MAS) is a cognitive system embedded into a physical device responsible for controlling the existing resources and communicability with other devices. An Embedded MAS provide autonomy and proactivity to physical devices using the BDI model. Designing a device implies choosing sensors and actuators as resources and programming both firmware and reasoning at design time. However, at runtime, resources could sometimes be damaged, presenting malfunctioning, or need to be changed. Then, performing predictive, preventive, or corrective maintenance at runtime is impossible since the designer must stop the Embedded MAS to swap resources and reprogram the system. This paper presents a novel ability for swapping resources at runtime in Embedded MAS using an extended version of Argo agents and the Jason framework. A case study with five scenarios tests the new swap ability in different situations: removing and changing existing resources, adding new known and unknown resources, and inserting a failure in a resource. The study case shows how the new swap ability can make devices with Embedded MAS adaptable and fault-tolerant.