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Since its dawn as a discipline, Artificial Intelligence (AI) has focused on mimicking the human mental processes. As AI applications matured, the interest for employing them into real-world complex systems (i.e., coupling AI with Cyber-Physical Systems—CPS) kept increasing. In the last decades, the multi-agent systems (MAS) paradigm has been among the most relevant approaches fostering the development of intelligent systems. In numerous scenarios, MAS boosted distributed autonomous reasoning and behaviors. However, many real-world applications (e.g., CPS) demand the respect of strict timing constraints. Unfortunately, current AI/MAS theories and applications only reason “about time” and are incapable of acting “in time” guaranteeing any timing predictability. This paper analyzes the MAS compliance with strict timing constraints (real-time compliance)—crucial for safety-critical applications such as healthcare, industry 4.0, and automotive. Moreover, it elicits the main reasons for the lack of real-time satisfiability in MAS (originated from current theories, standards, and implementations). In particular, traditional internal agent schedulers (general-purpose-like), communication middlewares, and negotiation protocols have been identified as co-factors inhibiting real-time compliance. To pave the road towards reliable and predictable MAS, this paper postulates a formal definition and mathematical model of real-time multi-agent systems (RT-MAS). Furthermore, this paper presents the results obtained by testing the dynamics characterizing the RT-MAS model within the simulator MAXIM-GPRT. Thus, it has been possible to analyze the deadline miss ratio between the algorithms employed in the most popular frameworks and the proposed ones. Finally, discussing the obtained results, the ongoing and future steps are outlined.

Real-time multi-agent systems: rationality, formal model, and empirical results