计算机科学技术学报 ›› 2022,Vol. 37 ›› Issue (1): 106-127.doi: 10.1007/s11390-021-1484-8

所属专题: Software Systems

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  • 收稿日期:2021-03-31 修回日期:2021-10-10 接受日期:2021-10-19 出版日期:2022-01-28 发布日期:2022-01-28

Meaningful Update and Repair of Markov Decision Processes for Self-Adaptive Systems

Wen-Hua Yang1,2,3 (杨文华), Member, CCF, Min-Xue Pan2 (潘敏学), Member, CCF, Yu Zhou1,2 (周宇), Senior Member, CCF, and Zhi-Qiu Huang1 (黄志球), Senior Member, CCF        

  1. 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    2State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
    3Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University, Nanjing 210023, China
  • Received:2021-03-31 Revised:2021-10-10 Accepted:2021-10-19 Online:2022-01-28 Published:2022-01-28
  • Contact: Wen-Hua Yang E-mail:ywh@nuaa.edu.cn
  • About author:Wen-Hua Yang is an assistant professor in the College of Computer Science and Technology at Nanjing University of Aeronautics and Astronautics, Nanjing. He received his Ph.D. degree in computer science and technology from Nanjing University, Nanjing, in 2017. His research interests include software engineering, self-adaptive software systems, and cyber-physical systems.
  • Supported by:
    This work was partially supported by the National Natural Science Foundation of China under Grant Nos.61802179, 61972193 and 61972197, the Fundamental Research Funds for the Central Universities of China under Grant No.NS2021069, and the Natural Science Foundation of Jiangsu Province of China under Grant No.BK20201292.


关键词: 自适应系统, 马尔可夫决策过程, 模型修复

Abstract: Self-adaptive systems are able to adjust their behaviour in response to environmental condition changes and are widely deployed as Internetwares. Considered as a promising way to handle the ever-growing complexity of software systems, they have seen an increasing level of interest and are covering a variety of applications, e.g., autonomous car systems and adaptive network systems. Many approaches for the construction of self-adaptive systems have been developed, and probabilistic models, such as Markov decision processes (MDPs), are one of the favoured. However, the majority of them do not deal with the problems of the underlying MDP being obsolete under new environments or unsatisfactory to the given properties. This results in the generated policies from such MDP failing to guide the self-adaptive system to run correctly and meet goals. In this article, we propose a systematic approach to updating an obsolete MDP by exploring new states and transitions and removing obsolete ones, and repairing an unsatisfactory MDP by adjusting its structure in a more meaningful way rather than arbitrarily changing the transition probabilities to values not in line with reality. Experimental results show that the MDPs updated and repaired by our approach are more competent in guiding the self-adaptive systems' correct running compared with the original ones.

Key words: self-adaptive system, Markov decision process, model repair

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