The self-adaptive system used in the business-critical field needs to comply with strict quality requirements. During the operation of the multi-agent adaptive system, it needs to realize adaptive adjustment according to the dynamic environment and requirements. Regarding the issue above, this paper proposed a Markov-based multi-agent adaptation run-time verification. The method first converted environmental impact factors in the multi-agent system into probabilistic form, then formalized the system into a Markov model. Finally, it conducted run-time verification through model checking so that the system could regulate itself through verification results. This paper conducted experiments through real cases of intelligent unmanned parking lots, and discussed the use process of the method and the verification effect. The results show that the method is suitable for adaptive systems with multi-agents, and can be adjusted in time when the system fails, and greatly improves the stability of the original system.

[2021-05-05] Printed Article /doc/2020.05.0198/v2