The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Two-Layer Approach to Developing Self-Adaptive Multi-Agent Systems in Open Environment
Abstract
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.
Related Content
Preethi, Sapna R., Mohammed Mujeer Ulla.
© 2023.
16 pages.
|
Srividya P..
© 2023.
12 pages.
|
Preeti Sahu.
© 2023.
15 pages.
|
Vandana Niranjan.
© 2023.
23 pages.
|
S. Darwin, E. Fantin Irudaya Raj, M. Appadurai, M. Chithambara Thanu.
© 2023.
33 pages.
|
Shankara Murthy H. M., Niranjana Rai, Ramakrishna N. Hegde.
© 2023.
23 pages.
|
Jothimani K., Bhagya Jyothi K. L..
© 2023.
19 pages.
|
|
|