Development of self-adaptive systems situated in open and uncertain environments is a great challenge in the community of software engineering due to the unpredictability of environment changes and the variety of self-adaptation manners. Explicit specification of expected changes and various self-adaptations at design-time, an approach often adopted by developers, seems ineffective. This paper presents an agent-based approach that combines two-layer self-adaptation mechanisms and reinforcement learning together to support the development and running of self-adaptive systems. The approach takes self-adaptive systems as multi-agent organizations and enables the agent itself to make decisions on self-adaptation by learning at run-time and at different levels. The proposed self-adaptation mechanisms that are based on organization metaphors enable self-adaptation at two layers: fine-grain behavior level and coarse-grain organization level. Corresponding reinforcement learning algorithms on self-adaptation are designed and integrated with the two-layer self-adaptation mechanisms. This paper further details developmental technologies, based on the above approach, in establishing self-adaptive systems, including extended software architecture for self-adaptation, an implementation framework, and a development process. A case study and experiment evaluations are conducted to illustrate the effectiveness of the proposed approach.
Retraction Note to: A Multi-agent Based Self-adaptive Genetic Algorithm for the Long-term Car Pooling Problem
