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Towards Multiple-Layer Self-Adaptations of Multi-Agent Organizations Using Reinforcement Learning
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Author(s): Xinjun Mao (National University of Defense Technology, China), Menggao Dong (National University of Defense Technology, China)and Haibin Zhu (Nipissing University, Canada)
Copyright: 2019
Pages: 30
Source title:
Novel Design and Applications of Robotics Technologies
Source Author(s)/Editor(s): Dan Zhang (York University, Canada)and Bin Wei (York University, Canada)
DOI: 10.4018/978-1-5225-5276-5.ch003
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Abstract
This chapter proposes a multi-agent organization model for self-adaptive software to examine the autonomous components and their self-adaptation that can be occurred at either the fine-grain behavior layer of a software agent or the coarse-grain organization layer of the roles that the agent plays. The authors design two-layer self-adaptation mechanisms and combine them with reinforcement learning together to tackle the uncertainty issues of self-adaptation, which enables software agents to make decisions on self-adaptation by learning at run-time to deal with various unanticipated changes. The reinforcement learning algorithms supporting fine-grain and coarse-grain adaptation mechanisms are designed. In order to support the development of self-adaptive software, the software architecture for individual agents, the development process and the software framework are proposed. A sample is developed in detail to illustrate our method and experiments are conducted to evaluate the effectiveness and efficiency of the proposed approach.
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