基于马尔可夫模型的多agent自适应在线验证

Multi-agent adaptive run-time verification based on Markov model

用于领域中业务关键型的自适应系统(self-adaptive system,SAS)需要遵从严格的质量要求,在多agent自适应系统运行过程中需要根据动态环境与需求实现自适应调整。针对上述问题,提出了一种基于马尔可夫的多agent自适应在线验证方法。首先将多agent系统中环境影响因素转换为概率形式,然后将系统形式化为马尔可夫模型,最后通过模型检查的方式进行在线验证,使系统通过验证结果对自身进行调控。通过现实的智能无人停车场案例进行了实验,论述了方法的使用流程以及验证效果。实验结果表明,该方法适用于具有多agent的自适应系统,并能够在系统出现故障情况下及时进行调控,相比原始系统的稳定性有了较大的改善。

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.