一句话：提出一种进化引导方法，将 MDP 模型转为参数化马尔可夫链，合成满足 QoS 需求的已验证帕累托最优策略。
该论文针对软件系统设计中满足复杂服务质量（QoS）组合要求的 Markov 决策过程（MDP）策略合成问题，提出一种进化引导方法。方法先将系统的 MDP 模型转换为参数化 Markov 链，再利用多目标遗传算法搜索定义 MDP 策略的帕累托最优参数取值。作者在基于服务的系统和机器人控制软件两个案例上评估，结果表明该方法能够处理现有概率模型检测器不支持的多种 QoS 要求组合；对于模型检测器支持的要求组合，也能依据既有质量指标生成更优的帕累托最优策略集合。
We present a new approach for synthesising Paretooptimal Markov decision process (MDP) policies that satisfy complex combinations of quality-of-service (QoS) software requirements. These policies correspond to optimal designs or configurations of software systems, and are obtained by translating MDP models of these systems into parametric Markov chains, and using multi-objective genetic algorithms to synthesise Pareto-optimal parameter values that define the required MDP policies. We use case studies from the service-based systems and robotic control software domains to show that our MDP policy synthesis approach can handle a wide range of QoS requirement combinations unsupported by current probabilistic model checkers. Moreover, for requirement combinations supported by these model checkers, our approach generates better Pareto-optimal policy sets according to established quality metrics.
@inproceedings{paperbot6628,
title = {Evolutionary-Guided Synthesis of Verified Pareto-Optimal MDP Policies},
author = {Simos Gerasimou and Javier Camara and Radu Calinescu and Naif Alasmari and Faisal Alhwikem and Xinwei Fang},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678727}
}