Minson: A Business Process Self-Adaptive Framework for Smart Office Based on Multi-agent

2014 ◽  
Author(s):  
Licong Zhu ◽  
Hongming Cai ◽  
Lihong Jiang
Keyword(s):  
Business Process ◽  
Multi Agent ◽  
Adaptive Framework ◽  
Self Adaptive

scholarly journals Toward a Self-Adaptive Cyberdefense Framework in Organization

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402098885
Author(s):  
Kuan-Jui Huang ◽  
Kuo-Huie Chiang
Keyword(s):  
Information System ◽  
Organizational Learning ◽  
Intelligent Agents ◽  
Coordination Mechanism ◽  
Learning Effects ◽  
Adaptive Model ◽  
Dynamic Propagation ◽  
The Future ◽  
Adaptive Framework ◽  
Self Adaptive

Organizations suffer more than ever from the inability to securely manage the information system, despite their myriad efforts. By introducing a real cyberattack of a bank, this research analyzes the characteristics of modern cyberattacks and simulates the dynamic propagation that makes them difficult to manage. It develops a self-adaptive framework that through simulation, distinctly improves cyberdefense efficiency. The results illustrate the discrepancies of the previous studies and validate the use of a time-based self-adaptive model for cybersecurity management. The results further show the significance of human and organizational learning effects and a coordination mechanism in obtaining a highly dependable cyberdefense setting. This study also provides an illuminating analysis for humans to position themselves in the collaborations with increasingly intelligent agents in the future.

A Two-Layer Approach to Developing Self-Adaptive Multi-Agent Systems in Open Environment

2014 ◽  
Vol 6 (1) ◽  
pp. 65-85 ◽  
Author(s):  
Xinjun Mao ◽  
Menggao Dong ◽  
Haibin Zhu
Keyword(s):  
Reinforcement Learning ◽  
Adaptive Systems ◽  
Multi Agent Systems ◽  
Uncertain Environments ◽  
Implementation Framework ◽  
Fine Grain ◽  
Adaptation Mechanisms ◽  
Multi Agent ◽  
Self Adaptation ◽  
Self Adaptive

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.

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