A Reinforcement Learning-Based Framework for the Generation and Evolution of Adaptation Rules

@article{Zhao2017ARL,
  title={A Reinforcement Learning-Based Framework for the Generation and Evolution of Adaptation Rules},
  author={Tianqi Zhao and Wei Zhang and Haiyan Zhao and Zhi Jin},
  journal={2017 IEEE International Conference on Autonomic Computing (ICAC)},
  year={2017},
  pages={103-112}
}
One of the challenges in self-adaptive systems concerns how to make adaptation to themselves at runtime in response to possible and even unexpected changes from the environment and/or user goals. A feasible solution to this challenge is rule-based adaptation, in which, adaptation decisions are made according to predefined rules that specify what particular actions should be performed to react to different changing events from the environment. Although it has the characteristic of highly… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 33 references

Rationalism with a dose of empiricism: Case-based reasoning for requirements-driven self-adaptation

2014 IEEE 22nd International Requirements Engineering Conference (RE) • 2014
View 2 Excerpts

Case-Based Reasoning

Springer Berlin Heidelberg • 2013
View 1 Excerpt

Managing non-functional uncertainty via model-driven adaptivity

2013 35th International Conference on Software Engineering (ICSE) • 2013
View 1 Excerpt

Similar Papers

Loading similar papers…